We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate. Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more. Links Mentioned in the Show:The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric SiegelGenerating ROI with AIBizML Cheat SheetGooderSurvey: Machine Learning Projects Still Routinely Fail to Deploy[Skill Track] MLOps Fundamentals

We don’t think about every decision we make. Some decisions are easy and intuitive, others can be riddled with doubt. In a business setting, decision-making is often crucial, and with that comes pressure to ensure we’re making the right decisions in the best way possible. We can often accompany decision-making with context, providing a narrative for how we might approach a decision, citing what data and insights have had significant input into our choices. But how do we approach storytelling and decision-making to breed success? There’s probably no better person to guide us through the ins and outs of decision-making than the co-author of Business Storytelling For Dummies.Lori L. Silverman is the owner of Partners for Progress, a management consulting firm. As a business strategist, she has consulted with organizations in fifteen industries including financial services, insurance, manufacturing and petroleum companies, government entities, and professional associations. As a keynote speaker, Lori has positively impacted the lives of thousands of people. She has appeared on over fifty radio and television shows to speak about using stories in the workplace and is the co-author of Critical SHIFT and Stories Trainers Tell. She’s a pioneer in the business storytelling field, author of five books, and is known worldwide for her work in collaborative data-informed decision-making.In the episode, Richie and Lori cover common problems in business decision-making, connecting decision-making to business processes, analytics and decision-making, integrating data practitioners and decision-makers, the role of data visualization and narrative storytelling, the SMARTER decision-making methodology, the importance of intuition, challenges faced when applying decision-making methodologies and much more. Links Mentioned in the ShowBusiness Storytelling For Dummies by Karen Dietz and Lori SilvermanConnect with Lori on LinkedinLevel Up with LoriBooks by LoriThe SMARTER Framework for Data-Informed Decision MakingMonetizing Data Through Informed, Collaborative Decision MakingThe Increasingly Vital Role of Business Storytelling in LeadershipPre-Suasion: A Revolutionary Way to Influence and Persuade by Robert Cialdini[Skill Track] Data Storytelling

Arianna Huffington, co-founder of The Huffington Post, woke up in a pool of blood nursing a broken cheekbone after collapsing at her desk in 2007. Various stresses and pressures in her life had manifested themself into an episode of extreme mental exhaustion. This event was the catalyst for her to write a book on well-being as well as start the behavioral-change company Thrive Global. Many of us have, or will, experience burnout at some point. The build-up of stress, negative emotions, and internal tension may not result in the same shocking scene Huffington found herself in, but its effects are serious and permeate not just through our profession but into our home life as well. Stress and burnout are especially prevalent in working environments where there is an emphasis on urgency, and with the constant advancements we’ve seen in the data & AI sphere in the past year, leaders and practitioners working in the data space will need to know how to recognize the symptoms of burnout and create workplace cultures that prevent burnout in the first place.Jen Fisher is Deloitte’s human sustainability leader. Previously, Fisher served as Deloitte’s first-ever chief well-being officer. She’s also a TEDx speaker, coauthor of the book, Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom Lines, editor-at-large for Thrive Global, and host of the “WorkWell” podcast series.In the episode, Jen and Adel cover Jen’s own personal experience with burnout, the role of a Chief Wellbeing Officer, the impact of work on our overall well-being, the patterns that lead to burnout, defining well-being in the workplace, technology’s impact on our well-being, psychological safety in the workplace, how managers and leaders can looking after themselves and their teams, the future of human sustainability in the workplace and much more. Links Mentioned in the Show:Work Better Together: How to Cultivate Strong Relationships to Maximize Well-Being and Boost Bottom LinesJen’s TED Talk: The Future of WorkBrené Brown: Clear Is Kind. Unclear Is Unkind.What Is Psychological Safety?

2023 was a huge year for data and AI. Everyone who didn't live under a rock started using generative AI, and much was teased by companies like OpenAI, Microsoft, Google and Meta. We saw the millions of different use cases generative AI could be applied to, as well as the iterations we could expect from the AI space, such as connected multi-modal models, LLMs in mobile devices and formal legislation. But what has this meant for DataCamp? What will we do to facilitate learners and organizations around the world in staying ahead of the curve?In this special episode of DataFramed, we sit down with DataCamp Co-Founders Jo Cornelissen, Chief Executive Officer, and Martijn Theuwissen, Chief Operating Officer, to discuss their expectations for data & AI in 2024.In the episode, Richie, Jo and Martijn discuss generative AI's mainstream impact in 2023, the broad use cases of generative AI and skills required to utilize it effectively, trends in AI and software development, how the programming languages for data are evolving, new roles in data & AI, the job market and skill development in data science and their predictions for 2024.Links Mentioned in the Show:Free course - Become an AI DeveloperWebinar - Data & AI Trends & Predictions 2024Courses:Artificial Intelligence (AI) StrategyGenerative AI for BusinessImplementing AI Solutions in BusinessAI Ethics

In January 2024, six activists were identified by British Police in London, suspected of planning to disrupt the London Stock Exchange through a lock-in. In an attempt to prevent the building from opening for trading. Despite the foiled attempt, the strategy for this protest was inherently flawed. Trading no longer requires a busy exchange with raucous shouting and phone calls to facilitate the flow of investment around the world. Nowadays, machines can trade at a fraction of a second, ingesting huge amounts of real-time data to execute finely tuned-trading strategies. But who programs these trading machines, how do we assess risk when trading at such a high volume and in such short periods of time?Anthony Markham is a Quant Developer in Algorithmic Trading and Risk Management in Sydney, Australia. With a background in Aerospace and Software Engineering, Anthony has experience in Data Science, facial recognition research, tertiary education, and Quantitative Finance, developing mostly in Python, Julia, and C++. When not working, Anthony enjoys working on personal projects, flying aircraft, and playing sports.In the episode, Richie and Anthony cover what algorithmic trading is, the use of machine learning techniques in trading strategies, the challenges of handling large datasets with low latency, risk management in algorithmic trading, data analysis techniques for handling time series data, the challenges of deep neural networks in trading, the diverse roles and skills of those who work in algorithmic trading and much more. Links Mentioned in the Show:Flash crash of 2010KDB+Q Query Language[Course] Quantitative Risk Management in PythonUnderstanding Value at Risk (VaR)

Cookies were invented to help online shoppers, simply as an identifier so that online carts weren’t lost to the ether. Marketers quickly saw the power of using cookies for more than just maintaining session states, and moved to use them as part of their targeted advertising. Before we knew it, our online habits were being tracked, without our clear consent. The unregulated cookie-boom lasted until 2018 with the advent of GDPR and the CCPA. Since then marketers have been evolving their practices, looking for alternatives to cookie-tracking that will perform comparatively, and with the cookie being phased out in 2024, technologies like fingerprinting and new privacy-centric marketing strategies will play a huge role in how products meet users in the future. Cory Munchbach has spent her career on the cutting edge of marketing technology and brings years working with Fortune 500 clients from various industries to BlueConic. Prior to BluConic, she was an analyst at Forrester Research where she covered business and consumer technology trends and the fast-moving marketing tech landscape. A sought-after speaker and industry voice, Cory’s work has been featured in Financial Times, Forbes, Raconteur, AdExchanger, The Drum, Venture Beat, Wired, AdAge, and Adweek. A life-long Bostonian, Cory has a bachelor’s degree in political science from Boston College and spends a considerable amount of her non-work hours on various volunteer and philanthropic initiatives in the greater Boston community. In the episode, Richie and Cory cover successful marketing strategies and their use of data, the types of data used in marketing, how data is leveraged during different stages of the customer life cycle, the impact of privacy laws on data collection and marketing strategies, tips on how to use customer data while protecting privacy and adhering to regulations, the importance of data skills in marketing, the future of marketing analytics and much more.Links Mentioned in the Show:BlueConicMattel CreationsGoogle: Prepare for third-party cookie restrictionsData Clean Rooms[Course] Marketing Analytics for Business

We’ve never been more aware of the word ‘hallucinate’ in a professional setting. Generative AI has taught us that we need to work in tandem with personal AI tools when we want accurate and reliable information. We’ve also seen the impacts of bias in AI systems, and why trusting outputs at face value can be a dangerous game, even for the largest tech organizations in the world. It seems we could be both very close and very far away from being able to fully trust AI in a work setting. To really find out what trustworthy AI is, and what causes us to lose trust in an AI system, we need to hear from someone who’s been at the forefront of the policy and tech around the issue. Alexandra Ebert is an expert in data privacy and responsible AI. She works on public policy issues in the emerging field of synthetic data and ethical AI. Alexandra is on Forbes ‘30 Under 30’ list and has an upcoming course on DataCamp! In addition to her role as Chief Trust Officer at MOSTLY AI, Alexandra is the chair of the IEEE Synthetic Data IC expert group and the host of the Data Democratization podcast.In the episode, Richie and Alexandra explore the importance of trust in AI, what causes us to lose trust in AI systems and the impacts of a lack of trust, AI regulation and adoption, AI decision accuracy and fairness, privacy concerns in AI, handling sensitive data in AI systems, the benefits of synthetic data, explainability and transparency in AI, skills for using AI in a trustworthy fashion and much more. Links Mentioned in the Show:MOSTLY.AIMicrosoft Research on AI FairnessUsing Synthetic Data for Machine Learning & AI in Python[Course] AI Ethics

Your data project doesn't end once you have results. In order to have impact, you need to communicate those results to others. Presentations filled with endless tables and technical jargon can easily become tedious, leading your audience to lose interest or misunderstand your point.Data storytelling provides a solution to this: by creating a narrative around your results you can increase engagement and understanding from your audience. This is an art, and there are so many factors that contribute to visualizing data and creating a compelling story, it can be overwhelming. However, with the right approach, creating data stories can become second nature. In this special episode of DataFramed, we join forces with the Present Beyond Measure podcast to glean the best data presentation practices from one of the leading voices in the space.Lea Pica host of the Founder and Host of the Present Beyond Measure podcast and is a seasoned digital analytics practitioner, social media marketer and blogger with over 11 years of experience building search marketing and digital analytics practices for companies like Scholastic, Victoria’s Secret and Prudential.Present Beyond Measure’s mission is to bring their teachings to the digital marketing and web analytics communities, and empower anyone responsible for presenting data to an audience.In the full episode, Richie and Lea cover the full picture of data presentation, how to understand your audience, leverage hollywood storytelling, data storyboarding and visualization, the use of imagery in presentations, cognitive load management, the use of throughlines in presentations, how to improve your speaking and engagement skills, data visualization techniques in business setting and much more. Links Mentioned in the Show:Present Beyond MeasureLea’s BookConnect with Lea on LinkedinHollywood Storytelling[Course] Data Storytelling Concepts

Data used to be the exhaust of our work activities, until we started seeing the value it can provide. Today, data is a strategic asset, used to gain a competitive advantage and well guarded from those that might use it to harm others. With this change in attitude, how we access and safeguard our data has improved massively. However, data breaches are not a thing of the past, and with the advent of AI, many new techniques for maliciously accessing data are being created. With the extra importance of data security, it is always pertinent to iterate on how we keep our data safe, and how we manage who has access to it. Bart Vandekerckhove is the co-founder and CEO at Raito. Raito is on a mission to bring back balance in data democratization and data security. Bart helps data teams save time on data access management, so they can focus on innovation. As the former PM Privacy at Collibra, Bart has seen first hand how slow data access management processes can harm progress. In the full episode, Richie and Bart explore the importance of data access management, the roles involved in data access including senior management’s role in data access, data security and privacy tools, the impact of AI on data security, how culture feeds into data security, the challenges of a creating a good data access management culture, common mistakes organizations make, advice for improving data security and much more. Links Mentioned in the Show:RaitoCapital One Data BreachOptus Data BreachIAMCourse: Introduction to Data Privacy

We learned so much about generative AI and its impact for people and organizations in 2023, we must anticipate many more innovations in the data and AI space 2024. One of the best places to look for this information is through the wisdom of those that spend their time with the Fortune 1000 leaders that are helping shape data and AI practices. Wavestone’s annual Data and AI Executive Leadership Survey is a great way to gain insight into thoughts in current practices, as well as understand what to expect from business leaders and organizations in the near future. In this episode, we speak to the author of the survey. Randy Bean is a start-up business founder, CEO, industry thought leader, author, and speaker in the field of data-driven business leadership.  He serves as Innovation Fellow, Data Strategy for Paris-based consultancy Wavestone. Randy is the creator of the Data and AI Leadership Executive Survey discussed in today's episode. He is the author of the bestselling "Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI", and a current contributor to Forbes, Harvard Business Review, and MIT Sloan Management Review.  In the episode, Richie and Randy explore the 2024 Data and AI Leadership Executive Survey, the impact of generative AI in 2023 and what to expect from it in 2024, the state of generative AI implementation in organizations, healthcare and AI, including examples of generative AI outperforming human doctors, the evolving responsibilities of CDOs, the increasing importance of data-driven decision-making in organizations, the barriers to becoming data-driven, insights on data skills and the generational shift towards more data-savvy business leaders, as well as much more. Links Mentioned in the Show:Data and AI Leadership Executive SurveyRandy’s Articles in ForbesAlly FinancialResponsible AI InstituteCourse: Implementing AI Solutions in Business

Regardless of profession, the work we do leaves behind a trace of actions that help us achieve our goals. This is especially true for those that work with data. For large enterprises where there are seemingly countless processes happening at any one time, keeping track of these processes is crucial. Given the scale of these processes, one small efficiency gain can leads to a staggering amount of time and money saved. Process mining is a data-driven approach to process analysis that uses event logs to extract process-related information. It can separate inferred facts, from exact truths, and uncover what really happens in a variety of operations. Wil van der Aalst is a full professor at RWTH Aachen University, leading the Process and Data Science (PADS) group. He is also the Chief Scientist at Celonis, part-time affiliated with the Fraunhofer FIT, and a member of the Board of Governors of Tilburg University. His research interests include process mining, Petri nets, business process management, workflow management, process modeling, and process analysis. Wil van der Aalst has published over 275 journal papers, 35 books (as author or editor), 630 refereed conference/workshop publications, and 85 book chapters.Cong Yu leads the CeloAI group at Celonis focusing on bringing advanced AI technologies to EMS products, building up capabilities for their knowledge platform, and ultimately helping enterprises in reducing process inefficiencies and achieving operational excellence.Previously, Cong was Principal (Research) Scientist / Research Director at Google Research NYC from September 2010 to July 2022, leading the NYSD/Beacon Research Group, and also taught at NYU Courant Institute of Mathematical Sciences. In the episode, Wil, Cong, and Richie explore process mining and its development over the past 25 years, the differences between process mining and ML, AI, and data mining, popular use cases of process mining, adoption from large enterprises like BMW, HP, and Dell, the requirements for an effective process mining system, the role of predictive analytics and data engineering in process mining, how to scale process mining systems, prospects within the field and much more.Links Mentioned in the Show:CelonisGartner’s Magic Quadrant for Process MiningPM4PyProcess Query Language (PQL)[Couse] Business Process Analytics in R

There are a few caveats to using generative AI tools, those caveats have led to a few tips that have quickly become second nature to those that use LLMs like ChatGPT. The main one being: have the domain knowledge to validate the output in order to avoid hallucinations. Hallucinations are one of the weak spots for LLMs due to the nature of the way they are built, as they are trained to correlate data in order to predict what might come next in an incomplete sequence. Does this mean that we’ll always have to be wary of the output of AI products, with the expectation that there is no intelligent decision-making going on under the hood? Far from it. Causal AI is bound by reason—rather than looking at correlation, these exciting systems are able to focus on the underlying causal mechanisms and relationships. As the AI field rapidly evolves, Causal AI is an area of research that is likely to have a huge impact on a huge number of industries and problems. Paul Hünermund is an Assistant Professor of Strategy and Innovation at Copenhagen Business School. In his research, Dr. Hünermund studies how firms can leverage new technologies in the space of machine learning and artificial intelligence such as Causal AI for value creation and competitive advantage. His work explores the potential for biases in organizational decision-making and ways for managers to counter them. It thereby sheds light on the origins of effective business strategies in markets characterized by a high degree of technological competition and the resulting implications for economic growth and environmental sustainability. His work has been published in The Journal of Management Studies, the Econometrics Journal, Research Policy, Journal of Product Innovation Management, International Journal of Industrial Organization, MIT Sloan Management Review, and Harvard Business Review, among others. In the full episode, Richie and Paul explore Causal AI, its differences when compared to other forms of AI, use cases of Causal AI in fields like drug development, marketing, manufacturing, and defense. They also discuss how Causal AI contributes to better decision-making, the role of domain experts in getting accurate results, what happens in the early stages of Causal AI adoption, exciting new developments within the Causal AI space and much more. Links Mentioned in the Show:Causal Data Science in BusinessCausal AI by causaLensIntro to Causal AI Using the DoWhy Library in PythonLesson: Inference (causal) models

Over the past year, we’ve seen a full hype cycle of hysteria and discourse surrounding generative AI. It almost seems difficult to think back to a time when no one had used ChatGPT. We are in the midst of the fourth industrial revolution, and technology is moving rapidly. Better performing and more capable models are being released at a stunning rate, and with the growing presence of multimodal AI, can we expect another whirlwind year that vastly changes the state of play within AI again? Who might be able to provide insight into what is to come in 2024?Craig S. Smith is an American journalist, former executive of The New York Times, and host of the podcast Eye on AI. Until January 2000, he wrote for The Wall Street Journal, most notably covering the rise of the religious movement Falun Gong in China. He has reported for the Times from more than 40 countries and has covered several conflicts, including the 2001 invasion of Afghanistan, the 2003 war in Iraq, and the 2006 Israeli-Lebanese war. He retired from the Times in 2018 and now writes about artificial intelligence for the Times and other publications. He was a special Government employee for the National Security Commission on Artificial Intelligence until the commission's end in October 2021. In the episode, Richie and Craig explore the 2023 advancements in generative AI, such as GPT-4, and the evolving roles of companies like Anthropic and Meta, practical AI applications for research and image generation, challenges in large language models, the promising future of world models and AI agents, the societal impacts of AI, the issue of misinformation, computational constraints, and the importance of AI literacy in the job market, the transformative potential of AI in various sectors and much more. Links Mentioned in the Show:Eye on AIWaveAnthropicCohereMidjourneyYann Lecun

Effective data management has become a cornerstone of success in our digital era. It involves not just collecting and storing information but also organizing, securing, and leveraging data to drive progress and innovation. Many organizations turn to tools like Snowflake for advanced data warehousing capabilities. However, while Snowflake enhances data storage and access, it's not a complete solution for all data management challenges. To address this, tools like Capital One’s Slingshot can be used alongside Snowflake, helping to optimize costs and refine data management strategies.Salim Syed is a VP, Head of engineering for Capital One Slingshot product. He led Capital One’s data warehouse migration to AWS and is a specialist in deploying Snowflake to a large enterprise. Salim’s expertise lies in developing Big Data (Lake) and Data Warehouse strategy on the public cloud. He leads an organization of more than 100 data engineers, support engineers, DBAs and full stack developers in driving enterprise data lake, data warehouse, data management and visualization platform services.Salim has more than 25 years of experience in the data ecosystem. His career started in data engineering where he built data pipelines and then moved into maintenance and administration of large database servers using multi-tier replication architecture in various remote locations. He then worked at CodeRye as a database architect and at 3M Health Information Systems as an enterprise data architect. Salim has been at Capital One for the past six years.In this episode, Adel and Salim explore cloud data management and the evolution of Slingshot into a major multi-tenant SaaS platform, the shift from on-premise to cloud-based data governance, the role of centralized tooling, strategies for effective cloud data management, including data governance, cost optimization, and waste reduction as well as insights into navigating the complexities of data infrastructure, security, and scalability in the modern digital era.Links Mentioned in the Show:Capital One SlingshotSnowflakeCourse: Introduction to Data WarehousingCourse: Introduction to Snowflake

There's often a debate in technology ethics on whether technology is neutral or not. On one hand, critics have rightfully pointed out examples of technology exacerbating the climate crisis, amplifying bias as we've seen in our recent episode with Dr. Joy Buolamwini, or contributing to the spread of misinformation and disinformation. Conversely, we cannot deny the many wonderful things technology has given us, from better healthcare outcomes, to the ability to communicate wherever we are in the world, or to elevate the quality of life of everyone on the planet.It is this duality, that today's guest, Marga Hoek, points to as to why technology is neutral, and why it is in our hands to use it for good.Marga Hoek is a true visionary on sustainable business, capital, and technology and a successful business leader. As a three-time CEO, Board Member, Chair, and Founder of Business for Good, she applies her vision on how business can be a true force for good in practice. As a bestselling and multi-award-winning author, member of Thinkers50, and one of the most in-demand speakers on sustainable business and ESG investment, Marga Hoek has inspired many companies and leaders worldwide. She is also appreciated as a global voice for G20 and G7 Intergovernmental forums, international climate meetings and COPs, and many other prestigious global conferences. In the episode, Adel and Marga explore the fourth industrial revolution and the eight technologies that combine to build it, the ethical application of technology and how it can be the biggest lever to combating climate change and building a sustainable society, how data and AI enable real-time information sharing leading to better early warning systems related to the environment, use cases of tech for good initiatives, how collaboration can bridge the gap in investment for sustainable business ventures and a lot more. Links Mentioned In the Show:Tech for GoodAzure FarmBeatsCapgemini in the Mojave DesertReDeTec 3D PrintingFramlab 3D Printed Homes for the Unsheltered

The consequences of data not being easily accessible within an organization are profound. Good decision-making often relies on good information, and with crucial insights locked behind closed doors, decision-makers may have to rely on incomplete information, stifling their ability to innovate through a lack of comprehensive data access or an inability to leverage data to its full potential. The ramifications of this are not merely operational – they extend to the core of an organization's ability to thrive in the data-driven era. However, democratizing access to data is only the first hurdle in driving a data led organization, employees need to feel confident in their ability to use data, try new tools and adopt new processes. But who is best to show us the benefits of accessing and utilizing data currently, and the cultural benefits it can bring. Lilac Schoenbeck is the Vice President of Strategic Initiatives at Rocket Software. Lilac has two decades of experience in enterprise software, data center technology and cloud, with wider experience in product marketing, pricing and packaging, corporate strategy, M&A integrations and product management. Lilac is passionate about delivering exceptional technology to IT teams that helps them drive value for their businesses. In the episode, Richie and Lilac explore data democratization and the importance of having widespread data capabilities across an organization, common data problems that data democratization can solve, tooling to facilitate better access and use of data, tool and process adoption, confidence with data, good data culture, processes to encourage good data usage and much more. Links mentioned in the showRocket SoftwareWhat Does Democratizing Data Mean? Unlocking the Power of Data CulturesDemocratizing Data in Large Enterprises[Course] Introduction to Data Culture

We are all guilty of getting excited about shiny new toys in whatever guise they present themselves to us. For many of us, lots of the recent shiny new toys have been ways of utilizing AI to update and iterate on the ways that we work. Leadership teams have been looking for ways that their organizations can incorporate AI solutions into their products, regardless of whether they might be the most valuable use of the company's time. A company that fails to incorporate new tools and technology will stagnate and fail altogether right? A failure to adapt to the new state of play will surely stop the company from becoming a high performer? Or will it? What sets apart high-performing organizations from their non high-performing counterparts?It’s not shiny new toys. It’s culture. Counter to conventional wisdom, the norms and beliefs of an organization, and not the technology and tools it uses, is what drives its performance.Andrew McAfee is a Principal Research Scientist at the MIT Sloan School of Management, co-founder and co-director of MIT’s Initiative on the Digital Economy, and the inaugural Visiting Fellow at the Technology and Society organization at Google. He studies how technological progress changes the world. His book, The Geek Way, reveals a new way to get big things done. His previous books include More from Less and, with Erik Brynjolfsson, The Second Machine Age.McAfee has written for publications including Foreign Affairs, Harvard Business Review, The Economist, The Wall Street Journal, and The New York Times. He's talked about his work on CNN and 60 Minutes, at the World Economic Forum, TED, and the Aspen Ideas Festival, with Tom Friedman and Fareed Zakaria, and in front of many international and domestic audiences. He’s also advised many of the world’s largest corporations and organizations ranging from the IMF to the Boston Red Sox to the US Intelligence Community.Throughout the episode, Adel and Andrew explore the four cultural norms of the Geek way, the evolutionary biological underpinnings of the traits high performing organizations exhibit, case studies in adapting organizational culture, the role of data in driving high performance teams, useful frameworks leaders can adopt to build high performing organizations, and a lot more. Link mentioned in the show:The Geek Way: The Radical Mindset That Drives Extraordinary Results by Andrew McAfeeThe Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Andrew McAfee and Erik BrynjolfssonThe Planning FallacyAnnie DukeSteven PinkerAdam Grant

Poor data engineering is like building a shaky foundation for a house—it leads to unreliable information, wasted time and money, and even legal problems, making everything less dependable and more troublesome in our digital world. In the retail industry specifically, data engineering is particularly important for managing and analyzing large volumes of sales, inventory, and customer data, enabling better demand forecasting, inventory optimization, and personalized customer experiences. It helps retailers make informed decisions, streamline operations, and remain competitive in a rapidly evolving market. Insight and frameworks learned from data engineering practices can be applied to a multitude of people and problems, and in turn, learning from someone who has been at the forefront of data engineering is invaluable.  Mohammad Sabah is SVP of Engineering and Data at Thrive Market, and was appointed to this role in 2018. He joined the company from Fama Technologies, where he served as Data Science and Engineering Advisor to Fama's CEO. He also served as Chief Data Scientist at The Honest Company following its acquisition of Insnap, which Sabah co-founded in 2015. Over the course of his career, Sabah has held various data science roles at companies including Facebook, Workday, Netflix, and Yahoo! In the episode, Richie and Mo explore the importance of using AI to identify patterns and proactively address common errors, the use of tools like dbt and SODA for data pipeline abstraction and stakeholder involvement in data quality, data governance and data quality as foundations for strong data engineering, validation layers at each step of the data pipeline to ensure data quality, collaboration between data analysts and data engineers for holistic problem-solving and reusability of patterns, ownership mentality in data engineering and much more. Links from the show:PagerDutyDomoOpsGeneCareer Track: Data Engineer

In 2015 an MIT Researcher set out to build a mirror that would augment their face to look like those of their idols. The execution of this went well, until it came to testing. When the researcher came to use the mirror, no face was detected. The researcher was not detected in the mirror, until that is, she put on a white mask, at which point, the mirror worked as expected. Three years later, a paper named ‘Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification’ was published by the same researcher. Its release started a wider conversation about bias within AI-based facial recognition systems, and about bias within AI in general. Work to fight against algorithmic bias, or ‘The Coded-Gaze’, has been ongoing since. But who spearheaded this work and highlighted these issues to the AI and tech community? Dr. Joy Buolamwini is an AI researcher, artist, and advocate. In 2023, she is one of Time’s top 100 most influential people in AI. Joy founded the Algorithmic Justice League to create a world with more equitable and accountable technology. Her TED Featured Talk on algorithmic bias has over 1.5 million views and in 2020 Netflix released the documentary ‘Coded Bias’ following Joy’s research into the flaws of facial recognition systems. Her MIT thesis methodology uncovered large racial and gender bias in AI services from companies like Microsoft, IBM, and Amazon. Her research has been covered in over 40 countries, and as a renowned international speaker she has championed the need for algorithmic justice at the World Economic Forum and the United Nations. She serves on the Global Tech Panel convened by the vice president of European Commission to advise world leaders and technology executives on ways to reduce the harms of A.I.As a creative science communicator, she has written op-eds on the impact of artificial intelligence for publications like TIME Magazine and New York Times. Her spoken word visual audit "AI, Ain't I A Woman?" which shows AI failures on the faces of iconic women like Oprah Winfrey, Michelle Obama, and Serena Williams as well as the Coded Gaze short have been part of exhibitions ranging from the Museum of Fine Arts, Boston to the Barbican Centre, UK. A Rhodes Scholar and Fulbright Fellow, Joy has been named to notable lists including Bloomberg 50, Tech Review 35 under 35, , Forbes Top 50 Women in Tech (youngest), and Forbes 30 under 30. She holds two masters degrees from Oxford University and MIT; and a bachelor's degree in Computer Science from the Georgia Institute of Technology. Fortune Magazine named her to their 2019 list of world's greatest leaders describing her as "the conscience of the A.I. Revolution."In the episode, Richie and Joy discuss her journey into AI, the ethics of AI, the inception of Joy’s interest in AI bias, the Aspire Mirror and Gender Shades projects, The Algorithmic Justice League, consequences of biased facial recognition systems, highlights from Joy’s book (Unmasking AI), challenges in AI research such as misleading datasets and the importance of context, balancing working in AI and data while being an artist, and much more. Links mentioned in the show:Unmasking AI by Joy BuolamwiniAlgorithmic Justice LeagueGender Shades ProjectThe Coded Gaze

I think it's safe to say that we are in the peak of the hype cycle with generative AI. Almost every week now, we see new startups with exciting new GenAI use-cases and products. However, exciting doesn't necessarily translate to useful. And now more than ever, it's important for leaders, whether at incumbents or startups, to adapt and drive value with generative AI and focus on useful use-cases. So how can they adapt well to these tectonic changes? Jason Feifer is the editor in chief of Entrepreneur magazine and host of the podcast Problem Solvers. Outside of Entrepreneur, he is the author of the book Build For Tomorrow, which helps readers find new opportunities in times of change, and co-hosts the podcast Help Wanted, where he helps solve listeners' work problems. He also writes a newsletter called One Thing Better, which each week gives you one better way to build a career or company you love.In the episode, Jason and Adel explore AI’s role in entrepreneurship, use cases and applications of AI, the effectiveness of certain AI tools, AI’s impact on established business models, frameworks for navigating change, advice for leaders and individuals on using AI in their work and much more. Links Mentioned in the Show:Build for Tomorrow by Jason FeiferOne Thing Better NewsletterHeyGenBurger King Accepting Credit Cards in the 90s[COURSE] Implementing AI Solutions in Business

Throughout the past year, we've seen AI go from a nice-to-have, to a must-have in almost every large organization’s boardroom. There’s been more and more focus deploy AI  by leadership teams, and as a result, there's never been more pressure on the data team to deliver with AI. However, as the pressure to deliver with AI grows, the need to build safe and trustworthy experiences has also never been more important. But how do we balance between innovation and building these trustworthy experiences? How do you make responsible AI practical? Who should we get into the room when scoping safe AI use-cases? Beena Ammanath is an award- winning senior technology executive with extensive experience in AI and digital transformation. Her career has spanned leadership roles in e-commerce, finance, marketing, telecom, retail, software products, service, and industrial domains. She is also the author of the ground breaking book, Trustworthy AI.Beena currently leads the Global Deloitte AI Institute and Trustworthy AI/ Ethical Technology at Deloitte. Prior to this, she was the CTO-AI at Hewlett Packard Enterprise. A champion for women and multicultural inclusion in technology and business, Beena founded Humans for AI, a 501c3b non-profit promoting diversity and inclusion in AI. Her work and contributions have been acknowledged with numerous awards and recognition such as 2016 Women Super Achiever Award from World Women’s Leadership Congress and induction into WITI’s 2017 Women in Technology Hall of Fame.Beena was honored by UC Berkeley as 2018 Woman of the Year for Business Analytics, by the San Francisco Business Times as one of the 2017 Most Influential Women in Bay Area and by the National Diversity Council as one of the Top 50 Multicultural Leaders in Tech.In the episode, Beena and Adel delve into the core principles of trustworthy AI, the interplay of ethics and AI in various industries, how to make trustworthy AI practical, who are the primary stakeholders for ensuring trustworthy AI, the importance of AI literacy when promoting responsible and trustworthy AI, and a lot more.Links mentioned in the ShowTrustworthy AI by Beena AmmanathDeloitte AI InstituteHumans for AIData Literacy by Design, with Valerie Logan, CEO of the Data Lodge[Course] Implementing AI Solutions in Business[Webinar - October 19th 2023] Building a Capability Roadmap for AI

In today's AI landscape, organizations are actively exploring how to seamlessly embed AI into their products, systems, processes, and workflows. The success of ChatGPT stands as a testament to this. Its success is not solely due to the performance of the underlying model; a significant part of its appeal lies in its human-centered user experience, particularly its chat interface. Beyond the foundational skills, infrastructure, and tools, it's clear that great design is a crucial ingredient in building memorable AI experiences.How do you build human-centered AI experiences? What is the role of design in driving successful AI implementations? How can data leaders and practitioners adopt a design lens when building with AI?Here to answer these questions is Haris Butt, Head of Product Design at ClickUp. ClickUp is a project management tool that's been making a big bet on AI, and Haris plays a key role in shaping how AI is embedded within the platform.Throughout the episode, Adel & Haris spoke about the role of design in driving human-centered AI experiences, the iterative process of designing with large language models, how to design AI experiences that promote trust, how designing for AI differs from traditional software, whether good design will ultimately end up killing prompt engineering, and a lot more.

It's been almost a year since ChatGPT was released, mainstreaming AI into the collective consciousness in the process. Since that moment, we've seen a really spirited debate emerge within the data & AI communities, and really public discourse at large. The focal point of this debate is whether AI is or will lead to existential risk for the human species at large.We've seen thinkers such as Elizier Yudkowski, Yuval Noah Harari, and others sound the alarm bell on how AI is as dangerous, if not more dangerous than nuclear weapons. We've also seen AI researchers and business leaders sign petitions and lobby government for strict regulation on AI. On the flip side, we've also seen luminaries within the field such as Andrew Ng and Yan Lecun, calling for, and not against, the proliferation of open-source AI. So how do we maneuver this debate, and where does the risk spectrum actually lie with AI? More importantly, how can we contextualize the risk of AI with other systemic risks humankind faces? Such as climate change, risk of nuclear war, and so on and so forth? How can we regulate AI without falling into the trap of regulatory capture—where a select and mighty few benefit from regulation, drowning out the competition in the meantime?Trond Arne Undheim is a Research scholar in Global Systemic Risk, Innovation, and Policy at Stanford University, Venture Partner at Antler, and CEO and co-founder of Yegii, an insight network with experts and knowledge assets on disruption. He is a nonresident Fellow at the Atlantic Council with a portfolio in artificial intelligence, future of work, data ethics, emerging technologies, and entrepreneurship. He is a former director of MIT Startup Exchange and has helped launch over 50 startups. In a previous life, he was an MIT Sloan School of Management Senior Lecturer, WPP Oracle Executive, and EU National Expert.In this episode, Trond and Adel explore the multifaceted risks associated with AI, the cascading risks lens and the debate over the likelihood of runaway AI. Trond shares the role of governments and organizations in shaping AI's future, the need for both global and regional regulatory frameworks, as well as the importance of educating decision-makers on AI's complexities. Trond also shares his opinion on the contrasting philosophies behind open and closed-source AI technologies, the risk of regulatory capture, and more. Links mentioned in the show:Augmented Lean: A Human-Centric Framework for Managing Frontline Operations by Trond Arne Undheim & Natan LinderFuture Tech: How to Capture Value from Disruptive Industry Trends Trond Arne UndheimFuturized PodcastStanford Cascading Risk StudyCourse: AI Ethics

From the dawn of humanity, decisions, both big and small, have shaped our trajectory. Decisions have built civilizations, forged alliances, and even charted the course of our very evolution. And now, as data & AI become more widespread, the potential upside for better decision making is massive. Yet, like any technology, the true value of data & AI is realized by how we wield it. We're often drawn to the allure of the latest tools and techniques, but it's crucial to remember that these tools are only as effective as the decisions we make with them. ChatGPT is only as good as the prompt you decide to feed it and what you decide to do with the output. A dashboard is only as good as the decisions that it influences. Even a data science team is only as effective as the value they deliver to the organization. So in this vast landscape of data and AI, how can we master the art of better decision making? How can we bridge data & AI with better decision intelligence?​​Cassie Kozyrkov founded the field of Decision Intelligence at Google where, until recently, she served as Chief Decision Scientist, advising leadership on decision process, AI strategy, and building data-driven organizations. Upon leaving Google, Cassie started her own company of which she is the CEO, Data Scientific. In almost 10 years at the company, Cassie personally trained over 20,000 Googlers in data-driven decision-making and AI and has helped over 500 projects implement decision intelligence best practices. Cassie also previously served in Google's Office of the CTO as Chief Data Scientist, and the rest of her 20 years of experience was split between consulting, data science, lecturing, and academia. Cassie is a top keynote speaker and a beloved personality in the data leadership community, followed by over half a million tech professionals. If you've ever went on a reading spree about AI, statistics, or decision-making, chances are you've encountered her writing, which has reached millions of readers. In the episode Cassie and Richie explore misconceptions around data science, stereotypes associated with being a data scientist, what the reality of working in data science is, advice for those starting their career in data science, and the challenges of being a data ‘jack-of-all-trades’. Cassie also shares what decision-science and decision intelligence are, what questions to ask future employers in any data science interview, the importance of collaboration between decision-makers and domain experts, the differences between data science models and their real-world implementations, the pros and cons of generative AI in data science, and much more. Links mentioned in the Show:Data scientist: The sexiest job of the 22nd centuryThe Netflix PrizeAI Products: Kitchen AnalogyType one, Two & Three Errors in StatisticsCourse: Data-Driven Decision Making for BusinessRadar: Data & AI Literacy...

In data science, the push for unbiased machine learning models is evident. So much effort is made into ensuring the products we create are done thoughtfully and correctly, but are we investing the same effort in ensuring our teams, the very architects of these models, are diverse and inclusive? Bias in data can lead to skewed results, and similarly, a lack of diversity in teams can result in narrow perspectives. As we prioritize building diversity and inclusion into our data, it's equally crucial to embed these principles within our teams. So, who is best equipped to guide us in integrating DEI from a data perspective?Tracy Daniels is the Chief Data Officer for Truist Financial Corporation. She leads the team responsible for Truist’s enterprise data capabilities, including strategy, governance, data platform delivery, client, master & reference data, and the centers of excellence for business intelligence visualization and artificial intelligence & machine learning. She is alsothe executive sponsor for Truist’s Enterprise Technology & Operations Diversity Council. Daniels joined Truist in 2018. She has more than 25 years of banking and technology experience leading high performing technology portfolio, development, infrastructure and global operations organizations. Tracy enjoys participating in civic and philanthropic endeavors including serving on the Georgia State University Foundation Board of Trustees. She has been recognized as a National 2013 WOC STEM Rising Star award recipient, the 2017 Working Mother magazine Mother of the Year recipient, and a 2021 Women In Technology (WIT) Women of the Year in STEAM finalist.In the episode Tracy and Richie discuss Truist's approach to Diversity, Equity, and Inclusion (DEI) and its alignment with the company's purpose and values, the distinction between diversity and inclusion, the positive outcomes of implementing DEI correctly, the importance of not missing opportunities both externally with customers and internally with talent, the significance of aligning diversity programs with business metrics and hiring to promote DEI, considerations for job advertisements that appeal to a diverse audience, and much more. Links mentioned in the show:McKinsey on Diversity and InclusionBrookings Piece on Mitigating Bias in DataAlgorithmic Justice LeagueEuropean Legislation on Data and DiversityCourse: AI EthicsRadar: Data & AI Literacy Edition

It's been a year since ChatGPT burst onto the scene. It has given many of us a sense of the power and potential that LLMs hold in revolutionizing the global economy. But the power that generative AI brings also comes with inherent risks that need to be mitigated. For those working in AI, the task at hand is monumental: to chart a safe and ethical course for the deployment and use of artificial intelligence. This isn't just a challenge; it's potentially one of the most important collective efforts of this decade. The stakes are high, involving not just technical and business considerations, but ethical and societal ones as well. How do we ensure that AI systems are designed responsibly? How do we mitigate risks such as bias, privacy violations, and the potential for misuse? How do we assemble the right multidisciplinary mindset and expertise for addressing AI safety? Reid Blackman, Ph.D., is the author of “Ethical Machines” (Harvard Business Review Press), creator and host of the podcast “Ethical Machines,” and Founder and CEO of Virtue, a digital ethical risk consultancy. He is also an advisor to the Canadian government on their federal AI regulations, was a founding member of EY’s AI Advisory Board, and a Senior Advisor to the Deloitte AI Institute. His work, which includes advising and speaking to organizations including AWS, US Bank, the FBI, NASA, and the World Economic Forum, has been profiled by The Wall Street Journal, the BBC, and Forbes. His written work appears in The Harvard Business Review and The New York Times. Prior to founding Virtue, Reid was a professor of philosophy at Colgate University and UNC-Chapel Hill.In the episode, Reid and Richie discuss the dominant concerns in AI ethics, from biased AI and privacy violations to the challenges introduced by generative AI, such as manipulative agents and IP issues. They delve into the existential threats posed by AI, including shifts in the job market and disinformation. Reid also shares examples where unethical AI has led to AI projects being scrapped, the difficulty in mitigating bias, preemptive measures for ethical AI and much more. Links mentioned in the show:Ethical Machines by Reid BlackmanVirtue Ethics ConsultancyAmazon’s Scrapped AI Recruiting ToolNIST AI Risk Management FrameworkCourse: AI EthicsDataCamp Radar: Data & AI Literacy

For the past few years, we've seen the importance of data literacy and why organizations must invest in a data-driven culture, mindset, and skillset. However, as generative AI tools like ChatGPT have risen to prominence in the past year, AI literacy has never been more important. But how do we begin to approach AI literacy? Is it an extension of data literacy, a complement, or a new paradigm altogether? How should you get started on your AI literacy ambitions? Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is a data analytics, AI, and BI thought leader and an expert with a flair for bridging business needs with technology. As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy.Cindi was previously a Gartner Research Vice President, the lead author for the data and analytics maturity model and analytics and BI Magic Quadrant, and a popular keynote speaker. She introduced new research in data and AI for good, NLP/BI Search, and augmented analytics, bringing both BI bake-offs and innovation panels to Gartner globally. She’s frequently quoted in MIT, Harvard Business Review, and Information Week. She is rated a top 12 influencer in big data and analytics by Analytics Insight, Onalytca, Solutions Review, and Humans of Data.In the episode, Cindi and Adel discuss how generative AI accelerates an organization’s data literacy, how leaders can think beyond data literacy and start to think about AI literacy, the importance of responsible use of AI, how to best communicate the value of AI within your organization, what generative AI means for data teams, AI use-cases in the data space, the psychological barriers blocking AI adoption, and much more. Links Mentioned in the Show:The Data Chief Podcast ThoughtSpot Sage BloombergGPT Radar: Data & AI LiteracyCourse: AI Ethics Course: Generative AI ConceptsCourse: Implementing AI Solutions in Business 

With September and International Literacy Day (September 8th) upon us, we’re dedicating the entire month to cover the ins and outs of data & AI literacy. Make sure to sign up for the events we have in store, and to tune in for this month’s episodes.Data & AI Literacy MonthDataCamp Radar: Data & AI Literacy Edition

The mainstreaming of data & AI is fundamentally altering the way we work and operate. But with rising innovation, comes rising ambiguity and complexity. How can leaders effectively navigate the path ahead? How can leaders adopt data-driven decision-making and learn from their mistakes? How can leaders use data to look inward, and become what today’s guest describes as “meta-leaders”? Constance Dierickx is an internationally recognized expert in high-stakes decision-making who has advised leaders and delivered speeches in more than 20 countries. Founder and president of CD Consulting Group, her clients include Fortune 20 companies, private equity firms, and large not-for-profits around the globe. She is a contributor to Harvard Business Review, Forbes, Chief Executive, and others, and has taught strategic decision-making at Skolkovo Institute of Science and Technology in Moscow, Russia. In the episode, Richie and Constance delve into what meta-leadership is, the nuances of meta-leadership, the pivotal role of data in leadership, the importance of recognizing subtle behavioral cues, the implications of cognitive biases (particularly overconfidence), and the essence of wisdom in decision-making. Constance also shares insights from her clinical psychology background, highlighting the application of biofeedback mechanisms in managing chronic pain and much more. Links From the Show:Meta-Leadership by Constance DierickxHigh-Stakes Leadership by Constance DierickxThe Merger Mindset by Constance DierickxDesign the Life You Love: A Step-by-Step Guide to Building a Meaningful FutureBook by Ayse BirselIntroducing The State of Data Literacy Report 2023Data-Driven Decision Making for Business

Throughout history, small businesses have consistently played a pivotal role in the global economy, serving as its foundational backbone. As we navigate the digital age, the emergence of large corporations and rapid technological advancements present new challenges. Now, more than ever, it's imperative for small businesses to adapt, embracing a data-driven approach to remain competitive and sustainable. In this evolving landscape, we need champions dedicated to guiding these businesses, ensuring they harness the full potential of modern tools and insights to ensure a fair and varied marketplace of goods and services for all. Dr Kendra Vant, Executive General Manager of Data & AI Products at Xero, is an industry leader in building data-driven products that harness AI and machine learning to solve complex problems for the small-business economy. Working across Australia, Asia and the US, Kendra has led data and technology teams at companies such as Seek, Telstra, Deloitte and now Xero where she leads the company's global efforts using emerging practices and technologies to help small businesses and their advisors benefit from the power of data and insights. Starting with doctoral research in experimental quantum physics at MIT and a stint building quantum computers at Los Alamos National Laboratory, Kendra has made a career of solving hard problems and pushing the boundaries of what's possible.In the episode, Kendra and Richie delve into the transformative impact of data science on small businesses, use-cases of data science for small businesses, how Xero has supported numerous small businesses with data science. They also cover the integration of AI in product development, the unexpected depth of data in seemingly low-tech sectors, the pivotal role of software platforms in data analysis and much more. Links Mentioned in The Show:XeroAnalyzing Business Data in SQLFinancial Modeling in SpreadsheetsImplementing AI Solutions in BusinessGenerative AI Concepts

As companies scale and become more successful, new horizons open, but with them come unexpected challenges. The influx of revenue and expansion of operations often reveal hidden complexities that can hinder efficiency and inflate costs. In this tricky situation, data teams can find themselves entangled in a web of obstacles that slow down their ability to innovate and respond to ever-changing business needs. Enter cloud analytics—a transformative solution that promises to break down barriers and unleash potential. By migrating analytics to the cloud, organizations can navigate the growing pains of success, cutting costs, enhancing flexibility, and empowering data teams to work with agility and precision. John Knieriemen is the Regional Business Lead for North America at Exasol, the market-leading high-performance analytics database. Prior to joining Exasol, he served as Vice President and General Manager at Teradata during an 11-year tenure with the company. John is responsible for strategically scaling Exasol’s North America business presence across industries and expanding the organization’s partner network. Solongo Erdenekhuyag is the former Customer Success and Data Strategy Leader at Exasol. Solongo is skilled in strategy, business development, program management, leadership, strategic partnerships, and management.In the episode, Richie, Solongo, and John cover the motivation for moving analytics to the cloud, economic triggers for migration, success stories from organizations who have migrated to the cloud, the challenges and potential roadblocks in migration, the importance of flexibility and open-mindedness and much more. Links from the ShowExasolAmazon S3Azure Blob StorageGoogle Cloud StorageBigQueryAmazon RedshiftSnowflake[Course] Understanding Cloud Computing[Course] AWS Cloud Concepts

Generative AI is here to stay—even in the 8 months since the public release of ChatGPT, there are an abundance of AI tools to help make us more productive at work and ease the stress of planning and execution of our daily lives among other things. Already, many of us are wondering what is to come in the next 8 months, the next year, and the next decade of AI’s evolution. In the grand scheme of things, this really is just the beginning. But what should we expect in this Cambrian explosion of technology? What are the use cases being developed behind the scenes? What do we need to be mindful of when training the next generations of AI? Can we combine multiple LLMs to get better results?Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage. Bal is also the Co-Founder of Aedeon—the first hyper-scale Marketplace for Data Analytics and AI talent.In the episode, Richie and Bal explore common use cases for generative AI, how it's evolving to solve enterprise problems, challenges of data governance and the importance of explainable AI, the challenges of tracking the lineage of AI and data in large organizations. Bal also touches on the shift from general-purpose generative AI models to more specialized models, fascinating use cases in the manufacturing industry, what to consider when adopting AI solutions in business, and much more.Links mentioned in the show:PulsarTrifactaAWS Clarify[Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business[Course] Generative AI Concepts

'Software is eating the world’ is a truism coined by Mark Andreesen, General Partner at Andreesen Horowitz. This was especially evident during the shift from analog mediums to digital at the turn of the century. Software companies have essentially usurped and replaced their non-digital predecessors. Amazon was the largest bookseller, Netflix was the largest movie "rental" service, Spotify or Apple were the largest music providers.Today, AI is starting to eat the world. However, we are still at the early start of the AI revolution, with AI set to become embedded in almost every piece of software we interact with. An AI ecosystem that touches every aspect of our lives is what today’s guest describes as ‘Ambient AI’. But what can we expect from this ramp up to Ambient AI? How will it change the way we work? What do we need to be mindful of as we develop this technology?Daniel Jeffries is the Managing Director of the AI Infrastructure Alliance and former CIO at Stability AI, the company responsible for Stable Diffusion, the popular open-source image generation model. He’s also an author, engineer, futurist, pro blogger and he’s given talks all over the world on AI and cryptographic platforms.In the episode, Adel and Daniel discuss how to define ambient AI, how our relationship with work will evolve as we become more reliant on AI, what the AI ecosystem is missing to rapidly scale adoption, why we need to accelerate the maturity of the open source AI ecosystem, how AI existential risk discourse takes away focus from real AI risk, and a lot lot more.Links Mentioned in the ShowDaniel’s Writing on MediumDaniel’s SubstackAI Infrastructure AllianceStability AIFrancois CholletRed Pajama DatasetRun AIWill Superintelligent AI End the World? By Eliezer Yudkowsky Nick Bostrom’s Paper Clip MaximizerThe pessimist archive [Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business

In a time when AI is evolving at breakneck speeds, taking a step back and gaining a bird's-eye view of the evolving AI ecosystem is paramount to understanding where the field is headed.With this bird's-eye view come a series of questions. Which trends will dominate generative AI in the foreseeable future? What are the truly transformative use-cases that will reshape our business landscape? What does the skills economy look like in an age of hyper intelligence?Enter Joanne Chen, General Partner at Foundation Capital. Joanne invests in early-stage AI-first B2B applications and data platforms that are the building blocks of the automated enterprise. She has shared her learnings as a featured speaker at conferences, including CES, SXSW, WebSummit, and has spoken about the impact of AI on society in her TED talk titled "Confessions of an AI Investor." Joanne began her career as an engineer at Cisco Systems and later co-founded a mobile gaming company. She also spent many years working on Wall Street at Jefferies & Company, helping tech companies go through the IPO and M&A processes, and at Probitas Partners, advising venture firms on their fundraising process.Throughout the episode, Richie and Joanne cover emerging trends in generative AI, business use cases that have emerged in the past year since the advent of tools like ChatGPT, the role of AI in augmenting work, the ever-changing job market and AI's impact on it, as well as actionable insights for individuals and organizations wanting to adopt AI.Links mentioned in the show:JasperAIAnyScaleCerebras[Course] Introduction to ChatGPT[Course] Implementing AI Solutions in Business[Course] Generative AI Concepts

Spreadsheets have been the unsung heroes of the data world for many decades now. Yet, despite their ubiquity and importance, they've seen little disruption or evolution. The grid of cells we interact with today isn't far removed from the ones our predecessors used in the 1980s.However, the winds of change have started to blow. As we stand on the cusp of a new era in data and AI, the humble spreadsheet is poised for transformation. The coming changes could redefine how we interact with data, derive insights, and how we make decisions. The implications are vast given the popularity and dependence we have on spreadsheets, and the potential impacts could ripple through every corner of the professional world. Hjalmar Gislason is the founder and CEO of GRID, with their main product being a smart spreadsheet with an interactive data visualization layer and integrated AI assistance. Hjalmar previously served as VP of Product Management at Qlik. He was the founder and CEO of DataMarket, founded in 2008 and sold to Qlik in 2014. A career data nerd and entrepreneur, GRID is Hjalmar’s fifth software startup as a founder. In the episode, Richie and Hjalmar explore the integral role of spreadsheets in today's data-driven world, the limitations of traditional Business Intelligence tools, and the transformative potential of generative AI in the realm of spreadsheets.

Data and AI are advancing at an unprecedented rate—and while the jury is still out on achieving superintelligent AI systems, the idea of artificial intelligence that can understand and learn anything—an “artificial general intelligence”—is becoming more likely. What does the rise of AI mean for the future of software and work as we know it? How will AI help reinvent most of the ways we interact with the digital and physical world?Bob Muglia is a data technology investor and business executive, former CEO of Snowflake, and past president of Microsoft's Server and Tools Division. As a leader in data & AI, Bob focuses on how innovation and ethical values can merge to shape the data economy's future in the era of AI. He serves as a board director for emerging companies that seek to maximize the power of data to help solve some of the world's most challenging problems.In the episode, Richie and Bob explore the current era of AI and what it means for the future of software. Throughout the episode, they discuss how to approach driving value with large language models, the main challenges organizations face when deploying AI systems, the risks, and rewards of fine-tuning LLMs for specific use cases, what the next 12 to 18 months hold for the burgeoning AI ecosystem, the likelihood of superintelligence within our lifetimes, and more. Links from the show:The Datapreneurs by Bob Muglia and Steve HammThe Singularity is Near by Ray KurzweilIsaac AsimovSnowflakePineconeDocugamiOpenAI/GPT-4The Modern Data Stack

Today's government agencies face unprecedented complexities, and when thinking about the role of government in driving positive change for society at large, data & AI stand out as key levers to empower government agencies to do more with less. However, the road to government data & AI transformation is fraught with risk, and is full with opportunity. So how can government data leaders succeed in their transformation endeavors? Steve Orrin is Intel’s Federal Chief Technology Officer. He leads Public Sector Solution Architecture, Strategy, and Technology Engagements and has held technology leadership positions at Intel where he has led cybersecurity programs, products, and strategy. Steve was previously CSO for Sarvega, CTO of Sanctum, CTO and co-founder of LockStar, and CTO at SynData Technologies. He was named one of InfoWorld's Top 25 CTO's, received Executive Mosaic’s Top CTO Executives Award, is a Washington Exec Top Chief Technology Officers to Watch in 2023, was the Vice-Chair of the NSITC/IDESG Security Committee and was a Guest Researcher at NIST’s National Cybersecurity Center of Excellence (NCCoE). He is a fellow at the Center for Advanced Defense Studies and the chair of the INSA Cyber Committee.Throughout the episode, we talked about the unique challenges government face when driving value with data & AI, how agencies need to align their data ambitions with their actual mission, the nuances between data privacy laws between the united states, Europe, and China, how to best approach launching pilot projects if you are in government, and a lot more.

Every year we become increasingly aware of the urgency of the climate crisis, and with that, the need to usher in renewable energies and scale their adoption has never been more important. However, as we look at the ways to scale the adoption of renewable energy, data stands out as a key lever to accelerate a greener future. Today’s guest is Jean-Pierre Pélicier, CDO at ENGIE. ENGIE is one of the largest energy producers in the world and definitely one of the largest in Europe. They operate in more than 48 countries and have committed to becoming carbon neutral by 2045. Data plays a crucial part in these plans.In the episode, Jean-Pierre shares his unique perspective on how data is not just transforming the renewable energy industry but also redefining the way we approach the climate crisis. From harnessing the power of data to optimize energy production and distribution to leveraging advanced analytics to predict and mitigate environmental impacts, Jean-Pierre highlights the ways data continues to be an invaluable tool in our quest for a sustainable future.Also discussed in the episode are the challenges of data collection and quality in the energy sector, the importance of fostering a data culture within an organization, and aligning data strategy with a company's strategic objectives.

About 10 years ago, Thomas Davenport & DJ Patil published the article "Data Scientist: The Sexiest Job of the 21st Century" in the Harvard Business Review. In this piece, they described the bourgeoning role of the data scientist and what it will mean for organizations and individuals in the coming decade. As time has passed, data science has become increasingly institutionalized. Once seen as a luxury, it is now deemed a necessity in every modern boardroom. Moreover as technologies like AI and systems like ChatGPT keep astonishing us with their capabilities in handling data science tasks, it raises a pertinent question: Is Data Science Still the Sexiest Job of the 21st Century?In this episode, we invited Thomas Davenport on the show to share his perspective on where data science & AI are at today, and where they are headed. Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. One of HBR’s most frequently published authors, Thomas has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence.Throughout the episode, we discuss how data science has changed since he first published his article, how it has become more institutionalized, how data leaders can drive value with data science, the importance of data culture, his views on AI and where he thinks its going, and a lot more. Links from the Show:Working with AI by Thomas DavenportThe AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas DavenportHarvard Business ReviewNew Vantage PartnersCCC Intelligent SolutionsRadar AI

Historically in elite team sports, there has often been a dynamic between players and their inherent abilities, and the vision of the coach. In many sports, we’ve seen coaching strategies influence the future of how the game is played. As the era of professionalism swept across many elite sports in the 90s, we saw the highest-level sports teams achieve a competitive edge by looking at the data, with sports fans often noticing a difference in the ‘feel’ of the way their team plays. In Basketball specifically, we have recently seen the rise of the 3-pointer, a riskier and much more difficult shot to accurately hit, even for professional players. But what has driven the rise of the 3-pointer? Is it another trend among coaches, or does the answer lie with data-based insights and the analysts producing these insights?Seth Partnow is the Director of North American Sports at StatsBomb, where he previously served as their Director of Basketball Analytics. Prior to joining StatsBomb in 2021, Seth was the Director of Basketball Research for the Milwaukee Bucks basketball team. Seth is also an accomplished Analyst and Author, having worked as an NBA Analyst for The Athletic since 2019 and having published his own book on basketball analytics, The Midrange Theory. Seth’s knowledge and insight bridges the gap between data analytics and elite US sport. In the episode, Seth and Richie look into the intricate dynamics of elite basketball. Seth explores the challenges of attributing individual contributions in a sport where the outcome is significantly influenced by the complex interplay between players.Drawing from his extensive experience in the field, Seth discusses the complexities of analyzing player performance, the nuances of determining why certain players get easier or harder shots, and the difficulty of attributing credit for defensive achievements to individual players.Seth provides a comprehensive overview of the various roles within sports analytics, from data engineers to analysts, and highlights the importance of finding one's niche within these roles, particularly in the context of elite basketball.Seth also shares his personal journey into basketball analytics, offering valuable insights and advice for those interested in pursuing a career in this field, stressing the importance of introspection and understanding the unique lifestyle associated with working for a sports team, while also offering industry-agnostic advice on how to approach analyzing and using data in any context.

In the realm of Applied Intelligence, Accenture leads the way in harnessing the power of data and AI to transform industries. From consumer products to life sciences, retail, and aerospace, Accenture's influence is far-reaching. But what drives the organization? How does it navigate the complex landscape of data modernization and transformation? And more importantly, how does it leverage technology not just as an enabler, but as a catalyst for innovation? Tracy Ring leads Accenture’s Applied Intelligence Products Category Group, in this role she has leadership across Consumer and Industrial Products, Automotive, Life Sciences, Retail and Aerospace and Defense. As the CDO and Global Generative AI lead for Life Sciences, she personally anchors the NA Applied Intelligence Life Sciences practice of more than 500 practitioners. Tracy has created solutions for Generative AI, Data led transformation, Artificial Intelligence, Data and Cloud Modernization, Analytics, and the organization and operating model strategies for next-generation adoption and AI fluency. In the episode, Tracy initially clarifies the difference between data modernization and data transformation, highlighting their distinct meanings and why the terms aren’t interchangeable. Tracy also emphasizes the importance of involving business end-users from the outset of data projects as well as advocating for a product-oriented approach to data.The discussion also covers the topic of team diversity and inclusivity. Tracy shares practical advice on how to build diverse teams and create an environment that encourages curiosity and open dialogue. Tracy also shares her perspective on the future of work and the importance of fostering meaningful conversations in the workplace. She advocates for an attitude of infinite curiosity within teams.In the context of life sciences, Tracy highlights the high stakes involved and underscores the need for responsible AI, data sharing, and data privacy. She also points out that the challenges in this field are more similar than dissimilar to those in other industries.Tune in for a wealth of insights from a seasoned leader in the field of Applied Intelligence.

A lot of the times when we walk into a supermarket, we don't necessarily think about the impact data science had in getting these products on shelves. However, as you’ll learn in today's episode, it's safe to say there's a myriad of applications for data science in the FMCG industry. Whether be that supply chain use-cases that leverage time-series forecasting techniques, to computer vision use-cases for on-shelf optimization—the use-cases are endless here. So how can data scientists and data leaders maximize value in this space?Enter Anastasia Zygmantovich. Anastasia is a Global Data Science Director at Reckitt, which is most known for products like Airwick, Lysol, Detol, and Durex. Throughout the episode, we discuss how data science can be used in the FMCG industry, how data leaders can hire impactful data teams in this space, why FMCG is a great place to work in for data scientists, some awesome use-cases she's worked on, how data scientists can best maximize their value in this space, what generative AI means for organizations, and a lot more.

When we think about video games like Call of Duty, Fifa, or Fortnite, our minds often turn to creative artists, software developers, designers, and producers. These are the people who make our favorite games a reality. But behind the scenes, data & AI actively shape our experience with our favorite video games. From the quality of video games, the accessibility of maps and worlds, even the go to market, data & AI play an impactul role in making or breaking the success of a video game.Marie de Léséleuc is an accomplished game industry professional with over a decade of experience. Marie started her career as a data analyst, and has since risen through the ranks to a data leader in the gaming industry. She's worked at companies such as Ubisoft, Warner Brothers, and most recently at Eidos, the company most well known for games such as Guardians of the Galaxy and Tomb Raider.Throughout the episode, we discuss how data science can be used in gaming, the unique challenges data teams face in gaming from really low data volumes to massive changes to production schedules and game vision. We also spoke about the difference between "AI" as we know it in data science, and AI in gaming, which informs how NPCs behave in a video game world—and a lot more.

Imagine making parenting choices not just based on instinct and through the lived experiences of others, but instead using data-driven techniques garnered through a career in data and economics. Emily Fair Oster is a Professor of Economics and International and Public Affairs at Brown University. Her work is unique, blending economics, health, and research in new ways. In her books "Expecting Better," "The Family Firm," and "Cribsheet," she's shown how data can help guide us through pregnancy and parenting.In the episode, Emily shows how she used her knowledge of data and economics when she was pregnant, and how this way of thinking can change how we make decisions.We look at the tension between what we feel and what the data tells us when we're making parenting choices, and why many of us lean on personal experiences. Emily tells us why it's important to use quality data when making decisions and how to make sense of all the information out there.Emily talks about the ins and outs of using data to make parenting decisions, discussing the big milestones in a child's life, the role of sleep, and how these can impact a person's future as well as the nuance in applying data-driven decision-making to your parenting. Emily also touches on how having two working parents and traditional gender roles can shape how we parent.Finally, Emily gives some helpful tips on finding and understanding good-quality data. This will help you make better decisions as a parent. Tune in for a thought-provoking look at parenting, data, and economics.

Although many have been cognizant of AI’s value in recent months, the further back we look, the more exclusive this group of people becomes. In our latest AI-series episodes of DataFramed, we gain insight from an expert who has been part of the industry for 40 years.Joaquin Marques, Founder and Principal Data Scientist at Kanayma LLC has been working in AI since 1983. With experience at major tech companies like IBM, Verizon, and Oracle, Joaquin's knowledge of AI is vast. Today, he leads an AI consultancy, Kanayma, where he creates innovative AI products.Throughout the episode, Joaquin shares his insights on AI's development over the years, its current state, and future possibilities. Joaquin also shares the exciting projects they've worked on at Kanayma as well as what to consider when building AI products, and how ChatGPT is making chatbots better.Joaquin goes beyond providing insight into the space, encouraging listeners to think about the practical consequences of implementing AI, with Joaquin sharing the finer technical details of many of the solutions he’s helped build. Joaquin also shares many of the thought processes that have helped him move forward when building AI products, providing context on many practical applications of AI, both from his past and the bleeding edge of today.  The discussion examines the complexities of artificial intelligence, from the perspective of someone that has been focused on this technology for more than most. Tune in for guidance on how to build AI into your own company's products.

With the advances in AI products and the explosion of ChatGPT in recent months, it is becoming easier to imagine a world where AI and humans work seamlessly together—revolutionizing how we solve complex problems and transform our daily lives. This is especially the case for data professionals.In this episode of our AI series, we speak to Sarah Schlobohm, Head of AI at Kubrick Group. Dr. Schlobohm leads the training of the next generation of machine learning engineers. With a background in finance and consulting, Sarah has a deep understanding of the intersection between business strategy, data science, and AI. Prior to her work in finance, Sarah became a chartered accountant, where she honed her skills in financial analysis and strategy. Sarah worked for one of the world's largest banks, where she used data science to fight financial crime, making significant contributions to the industry's efforts to combat money laundering and other illicit activities. Sarah shares her extensive knowledge on incorporating AI within data teams for maximum impact, covering a wide array of AI-related topics, including upskilling, productivity, and communication, to help data professionals understand how to integrate generative AI effectively in their daily work.Throughout the episode, Sarah explores the challenges and risks of AI integration, touching on the balance between privacy and utility. She highlights the risks data teams can avoid when using AI products and how to approach using AI products the right way. She also covers how different roles within a data team might make use of generative AI, as well as how it might effect coding ability going forward.Sarah also shares use cases for those in non-data teams, such as marketing, while also highlighting what to consider when using outputs from GPT models. Sarah shares the impact chatbots might have on education calling attention to the power of AI tutors in schools.Sarah encourages people to start using AI now, considering the barrier to entry is so low, and how that might not be the case going forward. From automating mundane tasks to enabling human-AI collaboration that makes work more enjoyable, Sarah underscores the transformative power of AI in shaping the future of humanity.Whether you're an AI enthusiast, data professional, or someoone with an interest in either this episode will provide you with a deeper understanding of the practical aspects of AI implementation.

With the advent of any new technology that promises to make humans lives easier, replacing concious actions with automation, there is always backlash. People are often aware of the displacement of jobs, and often, it is viewed in a negative light. But how do we try to change the collective understanding to one of hope and excitement? What use cases can be shared that will change the opinion of those that are weary of AI? Noelle Silver Russell is the Global AI Solutions & Generative AI & LLM Industry Lead at Accenture, responsible for enterprise-scale industry playbooks for generative AI and LLMs. In this episode of our AI series, Noelle discusses how to prioritize ChatGPT use cases by focusing on the different aspects of value creation that GPT models can bring to individuals and organizations. She addresses common misconceptions surrounding ChatGPT and AI in general, emphasizing the importance of understanding their potential benefits and selecting use cases that maximize positive impact, foster innovation, and contribute to job creation.Noelle draws parallels between the fast-moving AI projects today and the launch of Amazon Alexa, which she worked on, and points out that many of the discussions being raised today were also talked about 10 years ago. She discusses how companies can now use AI to focus both on business efficiencies and customer experience, no longer having to settle for a trade-off between the two.Noelle explains the best way for companies to approach adding GPT tools into their processes, which focusses on taking a holistic view to implementation. She also recommends use-cases for companies that are just beginning to use AI, as well as the challenges they might face when deploying models into production, and how they can mitigate them. On the topic of the displacement of jobs, Noelle draws parallels from when Alexa was launched, and how it faced similar criticisms, digging into the fear that people have around new technology, which could be transformed into enthusiasm. Noelle suggests that there is a burden on leadership within organizations to create a culture where people are excited to use AI tools, rather than feeling threatened by them.

ChatGPT has leaped into the forefront of our lives—everyone from students to multinational organizations are seeing value in adding a chat interface to an LLM. But OpenAI has been concentrating on this for years, steadily developing one of the most viral digital products this century. In this episode of our AI series, we sit down with Logan Kilpatrick. Logan currently leads developer relations at OpenAI, supporting developers building with DALL-E, the OpenAI API, and ChatGPT. Logan takes us through OpenAI’s products, API, and models, and provides insights into the many use cases of ChatGPT. Logan provides fascinating information on ChatGPT’s plugins and how they can be used to build agents that help us in a variety of contexts. He also discusses the future integration of LLMs into our daily lives and how it will add structure to the unstructured nature and difficult-to-leverage data we generate and interact with on a daily basis. Logan also touches on the powerful image input features in GPT4, how it can help those with partial sight to improve their quality of life, and how it can be used for various other use cases.Throughout the episode, we unpack the need for collaboration and innovation, due to ChatGPT becoming more powerful when integrated with other pieces of software. Covering key discussion points with regard to AI tools currently, in particular, what could be built in-house by OpenAI and what could be built in the public domain. Logan also discusses the ecosystem forming around ChatGPT and how it will all become connected going forward. Finally, Logan shares tips for getting better responses from ChatGPT and the things to consider when integrating it into your organization’s product. This episode provides a deep dive into the world of GPT models from within the eye of the storm, providing valuable insights to those interested in AI and its practical applications in our daily lives.

From May 8-11, discover expert insights from four industry leaders from OpenAI, Accenture, Kubrick Group, and Kanayma LLC on how to navigate the era of AI.

Ten years ago, Salesforce was trying to generate $1Bn of revenue in a quarter. Today, they create over $30Bn of revenue in year. Simultaneously, over the last decade we have seen huge advances in the world of data and data science.In this episode, Laura Gent Felker, Director of Data Insights and Scalability at Salesforce, talks about her experience in building and leading data teams within the organization over the last ten years. Laura shares her insights on how to create a learning culture within a team, how to prioritize projects while accounting for long-term strategy, and the importance of setting aside time for innovation.Laura also discusses how to ensure that the projects the team works on genuinely provide business value. She suggests creating a two-way street with executive leadership and understanding the collective value across a variety of stakeholders also citing that some of the best innovation she has seen come from her team is when they have had to solve high-priority short-term business problems. In addition, Laura shares a multi-layered approach to building a learning community within a data team. She explains that a culture of collaboration and trust is important in the direct data team, and the wider community within organizations. Laura also talks about the frameworks and mental models that can help develop business acumen. She highlights the importance of dedicating time to this area and being able to communicate insights effectively.Throughout the episode, Laura's insights provide valuable guidance for both junior and experienced data professionals, consumers and leaders in creating a learning culture, prioritizing projects, and building a strong data community within organizations.

Data literacy is becoming increasingly recognized as a valuable skill in today's workforce. We all interact with data on a daily basis, and organizations are now realizing the tremendous benefits of having a workforce that is well-versed in data, from interacting with dashboards to data analysis and data science. But, it all starts with data literacy. In this episode, we speak with Valerie Logan, CEO and Founder of The Data Lodge. Valerie is committed to data literacy, she believes that in today's digital society, data literacy is a life skill. With advisory services, bootcamps, a resource library and community services at The Data Lodge, Valerie is certifying the world’s first Data Literacy Program Leads and pioneering the path forward in cracking the data culture code. Valerie is also known for helping popularize the term "Data Literacy." In this episode, she shares insights on what a successful data literacy journey looks like, best practices for evangelizing data literacy programs, how to avoid siloed efforts between departments and much more.Valerie sheds light on the difficulties organizations face when trying to prioritize data literacy and data culture. She suggests that this is because humans are still at the center of organizations, and changing their behaviour is a challenge. She also talks about what data literacy means, and how the definition adapts to use cases. Valerie offers guidance on how to secure executive buy-in for data upskilling programs, explaining that finding a sponsor for the program is the first step. She also talks about the importance of extending buy-in to people who are less directly involved with data and upskilling, emphasizing how the program will help strategic objectives.Valerie also provides insights on the hallmarks of an effective pilot program for data literacy, suggesting that organizations go where there's already interest and that a good pilot is one where before and after effects can be measured. She also shares tips on how organizations can ensure that their data literacy program helps them achieve their strategic business goals.Throughout the episode, Valerie outlines the benefit and scope data literacy can have on an organization, with one of the most pertinent pieces of wisdom being a warning to organisations that risk ignoring upskilling and investing in data.Links mentioned in the show:RADAR 2023: Building an Enterprise Data Strategy that Puts People FirstThe Data LodgeThe State of Data Literacy in 2023What is Data Maturity and Why Does it Matter?

Building machine learning systems with high predictive accuracy is inherently hard, and embedding these systems into great product experiences is doubly so. To build truly great machine learning products that reach millions of users, organizations need to marry great data science expertise, with strong attention to user experience, design thinking, and a deep consideration for the impacts of your prediction on users and stakeholders. So how do you do that?Today’s guest is Sam Stone, Director of Product Management, Pricing & Data at Opendoor, a real-estate technology company that leverages machine learning to streamline the home buying and selling process. Sam played an integral part in developing AI/ML products related to home pricing including the Opendoor Valuation Model (OVM), market liquidity forecasting, portfolio optimization, and resale decision tooling. Prior to Opendoor, he was a co-founder and product manager at Ansaro, a SaaS startup using data science and machine learning to help companies improve hiring decisions. Sam holds degrees in Math and International Relations from Stanford and an MBA from Harvard.Throughout the episode, we spoke about his principles for great ML product design, how to think about data collection for these types of products, how to package outputs from a model within a slick user interface, what interpretability means in the eyes of customers, how to be proactive about monitoring failure points, and much more.

Ofcom is the government-approved regulatory and competition authority for the broadcasting, telecommunications and postal industries of the United Kingdom. It plays a vital role in ensuring TV, radio and telecoms work as they should. With vast swathes of information from a wide range of sources, data plays a huge role in the way Ofcom operates - in this episode, we learn the key drivers of Ofcom’s data strategy. Richard Davis is the Chief Data Officer at Ofcom, responsible for enabling data and analytics capabilities across the organisation. Prior to Ofcom, Richard worked as a Quantitative Analyst as well as being the former Head of Analytics and Innovation at LLoyds Bank, proving he has a wealth of experience across a variety of data roles. After joining Ofcom in 2022, Richard describes his experience of joining Ofcom, his ambition to bring in new processes, and how he leverages the community of data professionals. Richard also shares his advice for a new data leader, which includes understanding the pain points of the team, making insights more efficient, and keeping data teams aligned with the business's needs. He also elaborates on the key components of the data strategy at Ofcom, including aligning to good data, good people, and good decisions.Also discussed is the importance of cultural change in an organization and how to upskill data experts and train non-data specialists in data literacy, the difference between technical experts and people managers, and how organizations can enable people to grow to become technical leaders.Finally, Richard emphasizes the importance of evidence-based regulation, and how data literacy supports effective output. Richard provides excellent insight into the world of regulatory data, the challenges faced by Ofcom, and the solutions they can implement to overcome them.

The concept of literate programming, or the idea of programming in a document, was first introduced in 1984 by Donald Knuth. And as of today, notebooks are now the defacto tool for doing data science work. So as the data tooling space continues to evolve at breakneck speed, what are the possible directions the data science notebook can take? In this episode of DataFramed, we talk with Dr. Jodie Burchell, Data Science Developer Advocate at JetBrains, to find out how data science notebooks evolved into what they are today, what her predictions are for the future of notebooks and data science, and how generative AI will impact data teams going forward. Jodie completed a Ph.D. in clinical psychology and a postdoc in biostatistics before transitioning into data science. She has since worked for 7 years as a data scientist, developing products ranging from recommendation systems to audience profiling. She is also a prolific content creator in the data science community.Throughout the episode, Jodie discusses the evolution of data science notebooks over the last few years, noting how the move to remote-based notebooks has allowed for the seamless development of more complex models straight from the notebook environment.Jodie and Adel’s conversation also covers tooling challenges that have led to modern IDEs and notebooks, with Jodie highlighting the importance of good database tooling and visibility. She shares how data science notebooks have evolved to help democratize data for the wider organization, the tradeoffs between engineering-led approaches to tooling compared to data science approaches, what generative AI means for the data profession, her predictions for data science, and more.Tune in to this episode to learn more about the evolution of data science notebooks and the challenges and opportunities facing the data science community today.Links to mentioned in the show:DataCamp Workspace: An-in Browser Notebook IDEJetBrains' DataloreNick Cave on ChatGPT song lyrics imitating his styleGitHub Copilot More on the topic:The Past, Present, And Future of The Data Science NotebookHow to Use Jupyter Notebooks: The Ultimate Guide

In 2023, businesses are relying more heavily on data science and analytics teams than ever before. However, simply having a team of talented individuals is not enough to guarantee success. In the last of our RADAR 2023 sessions, Vijay Yadav and Vanessa Gonzalez will outline the keys to building high-impact data teams in 2023. They will discuss what are the hallmarks of a high-performing data team, the importance of diversity of background and skillset needed to build impactful data teams, setting up career pathways for data scientists, and more.Vijay Yadav is a highly respected data and analytics thought leader with over 20 years of experience in data product development, data engineering, and advanced analytics. As Director of Quantitative Sciences - Digital, Data, and Analytics at Merck, he leads data & analytics teams in creating AI/ML-driven data products to drive digital transformation. Vijay has held numerous leadership positions at various companies and is known for his ability to lead global teams to achieve high-impact results. Vanessa Gonzalez is the Sr. Director of Data Science and Innovation at Businessolver where she leads the Computational Linguistics, Machine Learning Engineering, Data Science, BI Analytics, and BI Engineering teams. She is experienced in leading data transformations, performing analytical and management functions that contribute to the goals and growth objectives of organizations and divisions. Listen in as Vanessa and Vijay share how to enable data teams to flourish in an ever-evolving data landscape. 

An effective data strategy is one that combines a variety of levers such as infrastructure, tools, organization, processes, and more. Arguably however, the most important aspect of a vibrant data strategy is culture and people.In the third of our four RADAR 2023 sessions, Cindi Howson and Valerie Logan discuss how data leaders can create a data strategy that puts their people at the center. Learn key insights into how to drive effective change management for data culture, how to drive adoption of data within the organization, common pitfalls when executing on a data strategy, and more. Cindi Howson is the Chief Data Strategy Officer at ThoughtSpot and host of The Data Chief podcast. Cindi is an analytics and BI thought leader and expert with a flair for bridging business needs with technology.  As Chief Data Strategy Officer at ThoughtSpot, she advises top clients on data strategy and best practices to become data-driven, speaks internationally on top trends such as AI ethics, and influences ThoughtSpot’s product strategy. Valerie Logan is the Founder and CEO of The Data Lodge. Valerie is committed to data literacy, she believes that in today's digital society, data literacy is a life skill. With advisory services, bootcamps, a resource library and community services at The Data Lodge, Valerie is certifying the world’s first Data Literacy Program Leads and pioneering the path forward in cracking the data culture code. In 2018, she was awarded Gartner’s Top Thought Leadership Award for her leadership in the area of Data Literacy.Listen in as Cindi and Valerie share how to build a data strategy that puts people first in an enterprise organization.

As organizations and the economy at large look to weather the challenges of 2023, data literacy is one of the keys to empowering organizations to navigate the decade's most significant challenges with confidence. In the second of our four RADAR 2023 sessions, Jordan Morrow shares how to navigate the future with data literacy, and how organizations can thrive as data becomes ever more prominent.Jordan is known as the "Godfather of Data Literacy", having helped pioneer the field by building one of the world's first data literacy programs and driving thought leadership on the subject. Jordan is Vice President and Head of Data And Analytics at BrainStorm, Inc., and a global trailblazer in the world of data literacy, building the world's first full scale data literacy program. He served as the Chair of the Advisory Board for The Data Literacy Project, has spoken at numerous conferences around the world and is an active voice in the data and analytics community. He has also helped companies and organizations around the world, including the United Nations, build and understand data literacy.Listen in as Jordan outlines how and why data literacy can help build individual and organizational resilience, how to scale data literacy within your organization, and more.

As organizations of all sizes continuously look to drive value out of data, the modern data stack has emerged as a clear solution for getting insights into the hands of the organization. With the rapid pace of innovation not slowing down, the tools within the modern data stack have enabled data teams to drive faster insights, collaborate at scale, and democratize data knowledge. However, are tools just enough to drive business value with data? In the first of our four RADAR 2023 sessions, we look at the key drivers of value within the modern data stack through the minds of Yali Sassoon and Barr Moses. Yali Sassoon is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. At Snowplow, Yali gets to combine his love of building things with his fascination of the ways in which people use data to reason.Barr Moses is CEO & Co-Founder of Monte Carlo. Previously, she was VP Customer Operations at customer success company Gainsight, where she helped scale the company 10x in revenue and, among other functions, built the data/analytics team. Listen in as Yali and Barr outline how data leaders can drive value creation with data in 2023.

Data leaders play a critical role in driving innovation and growth in various industries, and this is particularly true in highly regulated industries such as aviation. In such industries, data leaders face unique challenges and opportunities, working to balance the need for innovation with strict regulatory requirements. This week’s guest is Derek Cedillo, who has 27 years of experience working in Data and Analytics at GE Aerospace. Derek currently works as a Senior Manager for GE Aerospace’s Remote Monitoring and Diagnostics division, having previously worked as the Senior Director for Data Science and Analytics.In the episode, Derek shares the key components to successfully managing a Data Science program within a large and highly regulated organization. He also shares his insights on how to standardize data science planning across various projects and how to get a Data Scientists to think and work in an agile manner. We hear about ideal data team structures, how to approach hiring, and what skills to look for in new hires. The conversation also touches on what responsibility Data Leaders have within organizations, championing data-driven decisions and strategy, as well as the complexity Data Leaders face in highly regulated industries. When it comes to solving problems that provide value for the business, engagement and transparency are key aspects. Derek shares how to ensure that expectations are met through clear and frank conversations with executives that try to align expectations between management and Data Science teams. Finally, you'll learn about validation frameworks, best practices for teams in less regulated industries, what trends to look out for in 2023 and how ChatGPT is changing how executives define their expectations from Data Science teams. Links to mentioned in the show:The Checklist Manifesto by Atul GawandeTeam of Teams by General Stanley McChrystalThe Harvard Data Science Review PodcastRelevant Links from DataCamp:Article: Storytelling for More Impactful Data ScienceCourse: Data Communication ConceptsCourse: Data-Driven Decision-Making for Business

Oftentimes, Kaggle competitions are looked at as an excellent way for data scientists to sharpen their machine learning skills and become technically excellent. This begs the question, what are the hallmarks of high-performing Kaggle competitors? What makes a Kaggle Grand Master?Today’s guest, Jean-Francois Puget PhD, distinguished engineer at NVIDIA, has achieved this impressive feat three times. Throughout the episode, Richie and Jean-Francois discuss his background and how he became a Kaggle Grandmaster. He shares his scientific approach to machine learning and how he uses this to consistently achieve high results in Kaggle competitions.Jean-Francois also discusses how NVIDIA employs nine Kaggle Grandmasters and how they use Kaggle experiments to breed innovation in solving their machine learning challenges. He expands on the toolkit he employs in solving Kaggle competitions, and how he has achieved 50X improvements in efficiencies using tools like RAPIDS. Richie and Jean-Francois also delve into the difference between competitive data science on Kaggle and machine learning work in a real-world setting. They deep dive into the challenges of real-world machine learning, and how to resolve the ambiguities of using machine learning in production that data scientists don’t encounter in Kaggle competitions.

Studies have shown that companies lacking in racial diversity also have a corresponding lack in their ability to innovate as a whole, which makes it important for any organization to prioritize an inclusive workplace culture and welcome more women and underrepresented groups in data.This is why Nikiska Alcindor's work is so vital to the future of the data science industry. Nikisha is the President and Founder of the STEM Educational Institute (SEI), a nonprofit corporation that equips underrepresented high school students with the technological skills needed to build generational wealth and be effective in the workforce. Nikisha is a strategic management leader with expertise in organizational change, investing, and fundraising. She is a  recipient of the 2021 Dean Huss Teaching Award, a board member of the Upper Manhatten Empowerment Zone, and has taught a master class at Columbia Business School as well as several guest lectures at Columbia University.Throughout the episode, we discuss SEI’s three-pillar approach to education,  the rising importance of STEM-based careers, why financial literacy is crucial to a student’s success, SEI’s partnership with DataCamp, contextualizing educational and upskilling programs to your organization’s specific population, how data leaders can positively communicate upskilling initiatives, and much more.

In order for any data team to move from reactive to proactive and drive revenue for the business, they must make sure the basics are in place and that the team and data culture is mature enough to allow for scalable return on investment. Without these elements, data teams find themselves unable to make meaningful progress because they are stuck reacting to problems and responding to rudimentary questions from stakeholders across the organization. This quickly takes up bandwidth and keeps them from achieving meaningful ROI.In today’s episode, we have invited Shane Murray to break down how to effectively structure a data team, how data leaders can lead efficient decentralization, and how teams can scale their ROI in 2023. Shane is the Field CTO at Monte Carlo, a data reliability company that created the industry's first end-to-end Data Observability platform. Shane’s career has taken him through a successful 9-year tenure at The New York Times, where he grew the data analytics team from 12 to 150 people and managed all core data products. Shane is an expert when it comes to data observability, enabling effective ROI for data initiatives, scaling high-impact data teams, and more.Throughout the episode we discuss how to structure a data team for maximum efficiency, how data leaders can balance long-term and short-term data initiatives, how data maturity correlates to a team’s forward-thinking ability, data democratization with data insights and reporting ROI, best practices for change management, and much more.

The most common application for data science is to solve problems within your own organization, and as professionals become more data literate, they rely less and less on others to solve their problems and unlock professional growth and career advancement.But in the world of consulting, data science is used to solve other people’s problems, which adds an additional layer of complexity since consultants aren’t always given all of the tools they need to do the job right.Enter Pratik Agrawal, a Partner at Kearney Analytics leading the automotive and industrial transportation sector. In this episode, we are taking a look at how data science is applied in the consulting industry and what skills are critical to be a successful data science consultant. As a software engineer and data scientist with over a decade of experience in the consulting world at companies like Boston Consulting Group and IRI, Pratik has a deep understanding of how to navigate the industry and how data science can be leveraged in it, as well as expertise in digital transformation projects and strategy.Throughout the episode, we discuss common problems that consultants encounter, the skills needed to be successful as a consultant, the different approaches to analytics in consulting versus in an organization, how to handle context switching when juggling multiple projects, what makes consulting feel exciting and challenging, and much more.

One of the toughest parts of any data project is experimentation, not just because you need to choose the right testing method to confirm the project’s effectiveness, but because you also need to make sure you are testing the right hypothesis and measuring the right KPIs to ensure you receive accurate results.One of the most effective methods for data experimentation is A/B testing, and Anjali Mehra, Senior Director of Product Analytics, Data Science, Experimentation, and Instrumentation at DocuSign, is no stranger to how A/B testing can impact multiple parts of any organization. Throughout her career, she has also worked in marketing analytics and customer analytics at companies like Shutterfly, Wayfair, and Constant Contact.Throughout the episode, we discuss DocuSign’s analytics goals, how A/B testing works, how to gamify data experimentation, how A/B testing helps with new initiative validation, examples of A/B testing with data projects, how organizations can get started with data experimentation, and much more.

Perhaps the biggest obstacle to establishing a data culture is building trust in the data itself, making it vital for organizations to have a robust approach to data governance to ensure data quality is as high as possible.Enter Laurent Dresse, Data Governance Evangelist and Director of Professional Services at DataGalaxy. Throughout his career, Laurent has served as a bridge between IT and the rest of the business as an expert in data governance, quality, data management, and more. Throughout the episode, we discuss the state of data governance today, how data leaders and organizations can start their data governance journey, how to evangelize for data governance and gain buy-in across your organization, data governance tooling, and much more.

A special announcement from the DataFramed team. Join us for RADAR, a free two-day digital event curated to equip businesses and individuals with the insights to thrive in the era data, coming to you March 22-23, 2023! Register here to secure your spot!

Data quality can make or break any data initiative or product. If you aren’t able to collect data that is accurate, or you have data sets that have varying structures, or are filled with typos and other issues caused by human error, then the chances drop drastically that your data models will be accurate, or even helpful.When it comes to healthcare, data quality can be an absolute nightmare. With so many different data sources, high data churn rates, and a lack of standardization in many different healthcare categories, it can seem impossible to make quality healthcare more easily accessible to people when they need it.Ribbon Health seeks to change that by using AI to improve the quality of healthcare data and create a data platform with actionable provider information including insurance coverage, prices, and performance.Today’s guests are Nate Fox, the CTO, Co-Founder, and President of Ribbon Health, and Sunna Jo, a former pediatrician who is now a data scientist at Ribbon Health.Throughout the episode, we talk about why data quality in healthcare is messy, why having context around data is necessary to interpret and utilize it properly, how healthcare providers are improving their services because of platforms like Ribbon Health, how to tackle common data cleaning problems, and much more

When working with data, it’s easy for us to think about it as a mechanistic process, where data comes in and products come out. But as we’ve explored throughout the show, succeeding in data, whether you’re a data leader looking to build a data culture, a data scientist ascending the ranks, or even a policy maker looking to have an impact with data, the human side is crucial.At the heart of the “human side” is empathy— whether it’s for your stakeholders if you’re a data scientist developing a dashboard for them, empathy for your workforce if you’re a data or learning leader, or empathy for the planet and your citizens if you’re a policy maker. So how can we all practice better empathy? Specifically, can we all practice better data empathy? Luckily, empathy is a muscle that can be built. It’s not a “you have it, or you don’t” type of skill. So how can individuals and organizations utilize data empathy to improve how they work with data and the success rate of their projects?  Enter Phil Harvey, an Industrial Metaverse Architect in the Industrial Metaverse Core group at Microsoft. He is an expert in Data & AI Technical and Business Strategy & Philosophy. Harvey is also co-author of the book Data: A Guide to Humans, which explores the concept of Data Empathy, and how it can power better use of data through better communication and understanding of stakeholders in the value chain of data. 

Something we talk about alot on DataFramed is the importance of data literacy and data skills — and how they help both individuals and organizations succeed with data. Oftentimes, when organizations engage in upskilling programs on data literacy, one of the common pushbacks people have is, “I am not a numbers person”. So how do you move past that? How can leaders help their people bridge the data literacy gap, and in turn create a data culture?That’s where Dr. Selena Fisk comes in. Fisk is a data storyteller, coach, and thought leader in the data industry. She works in both the corporate sector and in education to develop data-led strategies that can help organizations grow. Fisk mainly specializes in the areas of data literacy, data visualization, and data storytelling, and is the author of three books, “Using and Analysing Data in Australian Schools,” “Leading Data-Informed Change in Schools,” and “I’m Not a Numbers Person: How to Make Good Decisions in a Data-Rich World.”Throughout our conversation, we discuss the difference between being data-informed and data-driven, the different levels of data literacy, why change management is crucial to the success of any data literacy program, how to democratize data skills, how to approach data upskilling as a leader, and much more.

Throughout 2022, there was an explosion in generative AI for images and text. GPT-3, DALLE-2, pointed us towards an AI-driven future. Recently, ChatGPT has taken the (data) world by storm — prompting many questions over how generative AI can be used in day to day activities. With the incredible amount of hype surrounding these new tools, we wanted to have a discussion grounded in how these tools are being operationalized today.Enter Scott Downes. Scott is the CTO of Invisible Technologies, a process automation platform that uses GPT-3 and other generative text technologies. Scott joins the show to talk about how organizations and data professionals can maximize the potential of these tools and how AI and humans can work together in a complementary fashion to optimize workflows, reduce time-intensive, tedious tasks, and do higher quality work. Scott has a decade of experience in technology, product engineering, and technical leadership, making a veteran in training and mentoring employees across the organization, whether their roles are more creative or more technical.Throughout the conversation, we talk about what Invisible Technologies uses GPT-3 to optimize workflows, a brief overview of GPT-3 and its use cases for working with text, how GPT-3 helps companies scale their operations, the promises of tools ChatGPT, how AI analysis and human review can work together to save lives, and much more.

In 2022, we saw significant developments in the field of data. From the emergence of generative AI to the growth of low-code data tools and AI assistants—these advancements signal an upcoming paradigm shift, where data-powered tools and machine learning systems will radically transform workflows across various professions.2022 also saw digital transformation remain a major theme for organizations across industries as they sought to embrace new ways of working, reaching customers, and providing value. As 2023’s looming economic uncertainty puts pressure on organizations to maximize ROI from their investments, digital and data transformation will continue to be one of the key levers by which organizations can cut costs and scale value for their stakeholders.So we’ve invited DataCamp’s co-founders, CEO Jonathan Cornelissen and COO Martijn Theuwissen to break down the top data trends they are seeing in the data space today, as well as their predictions for the future of the data industry.Jonathan Cornelissen is the CEO and co-founder of DataCamp. As the CEO of DataCamp, he helped grow DataCamp to upskill over 10M+ learners and 2800+ teams and enterprise clients. He is interested in everything related to data science, education and entrepreneurship. He holds a PhD in financial econometrics, and was the original author of an R package for quantitative finance.Martijn Theuwissen is the COO and co-founder of DataCamp. As the COO of DataCamp, he helps DataCamp’s enterprise clients on their data and digital transformation strategies, enabling them to make the most of DataCamp for Business’s offering, and helping them transform how their workforce uses data. 

Just as data is used to help businesses determine new directions, set new goals, and measure progress, data can be used in everyday life to help people do the same as they seek to improve themselves.As the new year arrives, many people are thinking about new goals and new ways to improve their lives, so we have invited Gary Wolf to the show to explore how you can use data-driven thinking to drive meaningful changes in yourself.Gary Wolf is the Co-Founder of The Quantified Self, an international community of makers and users of self-tracking tools. Prior to co-founding The Quantified Self, Wolf was a contributing editor for Wired Magazine, where he spent two decades covering the intersection of technology and culture, and his cover story in the New York Times is what introduced the general public to self-tracking as an emerging trend.In this episode, we talk about what The Quantified Self is, why self-tracking projects can be life-changing, how to get started with self-tracking, how to connect with others in the self-tracking community, and much more.

In programming, collaboration and experimentation can be very stressful, since sharing code and making it visible to others can be tedious, time-consuming, and nerve-wracking.Tools like Power BI are changing that entirely, by opening up new ways to collaborate between team members, add layers of customized and complex security to the data teams are working with, and making data much more accessible across organizations. Ginger Grant joins the show to talk about how organizations can utilize Power BI, Dax, and M to their fullest potential and create new opportunities for experimentation, innovation, and collaboration. Ginger is the Principal Consultant at the Desert Isle Group, working as an expert in advanced analytic solutions, including machine learning, data warehousing, ETL, reporting and cube development, Power BI, Excel Automation, Data Visualization and training. In addition to her consultant work, she is also a blogger at and global keynote speaker on developments and trends in data. Microsoft has also recognized her technical contributions by awarding her a MVP in Data Platform. In this episode, we talk about what Power BI is, the common mistakes organizations make when implementing Power BI, advanced use cases, and much more.

The insurance industry thrives on data from utilizing data and analytics to determine policy rates for customers to working with relevant partners in the industry to improve their products and services, data is embedded in everything that insurance companies do. But insurance companies also have a number of hurdles to overcome, whether it’s transitioning legacy data into new processes and technology, balancing new projects and models with ever-changing regulatory standards, and balancing the ethical considerations of how to best utilize data without resulting in unintended consequences for the end user. That’s why we’ve brought Rob Reynolds onto the show. Rob is the VP and Chief Data & Analytics Officer at W. R. Berkley, a multinational insurance holding company specializing in property and casualty insurance. Rob brings over two decades of experience in Data Science, IT, and technology leadership, with a particular expertise in building departments and establishing highly functioning teams, especially in highly dynamic environments. In this episode, we talk in-depth about how insurance companies utilize data, the most important skills for anyone looking for data science jobs in the insurance industry, why the need for thoughtful criticism is growing in data science, and how an expertise in communication will put you ahead of the pack.

With the increasing rate at which new data tools and platforms are being created, the modern data stack risks becoming just another buzzword data leaders use when talking about how they solve problems. Alongside the arrival of new data tools is the need for leaders to see beyond just the modern data stack and think deeply about how their data work can align with business outcomes, otherwise, they risk falling behind trying to create value from innovative, but irrelevant technology. In this episode, Yali Sassoon joins the show to explore what the modern data stack really means, how to rethink the modern data stack in terms of value creation, data collection versus data creation, and the right way businesses should approach data ingestion, and much more. Yali is the Co-Founder and Chief Strategy Officer at Snowplow Analytics, a behavioral data platform that empowers data teams to solve complex data challenges. Yali is an expert in data with a background in both strategy and operations consulting teaching companies how to use data properly to evolve their operations and improve their results.

2022 was an incredible year for Generative AI. From text generation models like GPT-3 to the rising popularity of AI image generation tools, generative AI has rapidly evolved over the last few years in both its popularity and its use cases. Martin Musiol joins the show this week to explore the business use cases of generative AI, and how it will continue to impact the way the society interacts with data. Martin is a Data Science Manager at IBM, as well as Co-Founder and an instructor at Generative AI, teaching people to develop their own AI that generates images, videos, music, text and other data. Martin has also been a keynote speaker at various events, such as Codemotion Milan. Having discovered his passion for AI in 2012, Martin has turned that passion into his expertise, becoming a thought leader in AI and machine learning space. In this episode, we talk about the state of generative AI today, privacy and intellectual property concerns, the strongest use cases for generative AI, what the future holds, and much more.

Data Analytics has played a major role in Chelsea’s journey to becoming the seventh most valuable football club in the world, Chelsea has won six league titles, eight FA Cups, five League Cups, and two Champions League titles. Today, we are going behind the scenes at Chelsea FC to see how they use data analytics to analyze matches, inform tactical decision-making, and drive matchday success in one of the world’s top football leagues, just in time for the 2022 FIFA World Cup in Qatar! Federico Bettuzzi is a Data Scientist at Chelsea FC. As a specialist in match analytics, Federico works with Chelsea’s first team to inform tactical decision making during matches. Federico joins the show to break down how he gathers and synthesizes data, how they develop match analyses for tactical reviews, how managers prioritize data analytics differently, how to balance long-term and short-term projects, and much more.

To become a data-driven organization, it takes a major shift in mindset and culture, investments in technology and infrastructure, skills transformation, and clearly evangelizing the usefulness of using data to drive better decision-making. With all of these levers to scale, many organizations get stuck early in their data transformation journey, not knowing what to prioritize and how. In this episode, Ganes Kesari joins the show to share the frameworks and processes that organizations can follow to become data-driven, measure their data maturity, and win stakeholder support across the organization. Ganes is Co-Founder and Chief Decision Scientist at Gramener, which helps companies make data-driven decisions through powerful data stories and analytics. He is an expert in data, analytics, organizational strategy, and hands-on execution. Throughout his 20-year career, Ganes has become an internationally-renowned speaker and has been published in Forbes, Entrepreneur, and has become a thought leader in Data Science. Throughout the episode, we talk about how organizations can scale their data maturity, how to build an effective data science roadmap, how to successfully navigate the skills and people components of data maturity, and much more.

During Data Literacy Month, we shared how data journalists curate and distill data stories to the wider public. Since 2020, Data Journalism has risen both in significance and visibility. Throughout the COVID-19 pandemic, data journalists have been instrumental in keeping the public informed by investigating, challenging, interpreting, and explaining complex datasets. In this episode, Betsy Ladyzhets joins the show to talk about the state of Data Journalism today, and shares from her experience as a data journalist Betsy is an independent science, health, and data journalist focused on COVID-19 and Founder of the COVID-19 Data Dispatch, an independent publication providing updates and resources on public COVID-19 data. She is also currently working as a Senior Journalism Fellow with the Documenting COVID-19 project at the Brown Institute for Media Innovation and MuckRock. Her work has been featured in Science News, FiveThirtyEight, MIT Tech Review, and the Covid Tracking Project. Throughout the show, we discuss the importance of letting data shape a narrative, what characteristics of traditional journalism are needed for data journalists, the best practices for delivering effective data stories, how the rise of AI and data visualization are impacting data journalism, and much more. Links shared during the episode: Data Sonification The COVID-19 Data Dispatch The Data Visualization Society Learning on DataCamp? Take part in this week’s XP-challenge: http://www.datacamp.com/promo/free-week-xp-challenge-2022

Python has dominated data science programming for the last few years, but there’s another rising star programming language seeing increased adoption and popularity—Julia. As the fourth most popular programming language, many data teams and practitioners are turning their attention toward understanding Julia and seeing how it could benefit individual careers, business operations, and drive increased value across organizations. Zacharias Voulgaris, PhD joins the show to talk about his experience with the Julia programming language and his perspective on the future of Julia’s widespread adoption. Zacharias is the author of Julia for Data Science. As a Data Science consultant and mentor with 10 years of international experience that includes the role of Chief Science Officer at three startups, Zacharias is an expert in data science, analytics, artificial intelligence, and information systems. In this episode, we discuss the strengths of Julia, how data scientists can get started using Julia, how team members and leaders alike can transition to Julia, why companies are secretive about adopting Julia, the interoperability of Julia with Python and other popular programming languages, and much more. Check out this month’s events: https://www.datacamp.com/data-driven-organizations-2022 Take the Introduction to Julia course for free! https://www.datacamp.com/courses/introduction-to-julia

While securing the support of senior executives is a major hurdle of implementing a data transformation program, it’s often one of the earliest and easiest hurdles to overcome in comparison to the overall program itself. Leading a data transformation program requires thorough planning, organization-wide collaboration, careful execution, robust testing, and so much more. Vanessa Gonzalez is the Senior Director of Data and Analytics for ML & AI at Transamerica. Vanessa has experience in data transformation, leadership, and strategic direction for Data Science and Data Governance teams, and is an experienced senior data manager. Vanessa joins the show to share how she is helping to lead Transamerica’s Data Transformation program. In this episode, we discuss the biggest challenges Transamerica has faced throughout the process, the most important factors to making any large-scale transformation successful, how to collaborate with other departments, how Vanessa structures her team, the key skills data scientists need to be successful, and much more. Check out this month’s events: https://www.datacamp.com/data-driven-organizations-2022

As data leaders continue to fill their talent gap, how should they approach sourcing, retaining, and upskilling their talent? What strategies should data leaders adopt in order to accomplish their talent goals and become data-driven? Kyle Winterbottom joins the show to talk about the key differentiators between data teams that build talent-dense teams and those that do not. Kyle is the host of Driven by Data: The Podcast, the Founder & CEO of Orbition, a talent solutions provider, for scaling Data, Analytics, & Artificial Intelligence teams across the UK, Europe and the USA. As an accomplished expert and thought leader in talent acquisition, attraction, and retention, as well as scaling data teams, Kyle was named one of Data IQ’s 100 Most Influential People in Data for 2022. In this episode, we talk about how data teams can position themselves to attract top talent, how to properly articulate how data team members are adding value to the business, how organizations can accidentally set data leaders up to fail, how to approach upskilling, and how data leaders can create an employer branding narrative to attract top talent. Check out this month’s events: https://www.datacamp.com/data-driven-organizations-2022

To improve Data Literacy, organizations need high-quality data training programs that give their employees the most valuable and relevant data skills they need. Many companies fall into the trap of implementing training programs that are poorly designed or not relevant for the needs of their learners.Sharon Castillo is the VP of Global Education at DataRobot, where she developed the DataRobot University, a self-service education portal that features both free and paid courses on AI and machine learning that are available to the public. With over 30 years of experience, Sharon is a leading expert in data training and employee upskilling programs, from development through execution.Sharon joins the show to talk about what makes an effective data training program, how to ensure employees retain the information, how to properly incentivize training participation, why organizations should prioritize training, and much more. This is essential listening for anyone developing a training program for their team or organization.

We have had many guests on the show to discuss how different industries leverage data science to transform the way they do business, but arguably one of the most important applications of data science is in space research and technology. Justin Fletcher joins the show to talk about how the US Space Force is using deep learning with telescope data to monitor satellites, potentially lethal space debris, and identify and prevent catastrophic collisions. Justin is responsible for artificial intelligence and autonomy technology development within the Space Domain Awareness Delta of the United States Space Force Space Systems Command. With over a decade of experience spanning space domain awareness, high performance computing, and air combat effectiveness, Justin is a recognized leader in defense applications of artificial intelligence and autonomy. In this episode, we talk about how the US Space Force utilizes deep learning, how the US Space Force publishes its research and data to find high-quality peer review, the must-have skills aspiring practitioners need in order to pursue a career in Defense, and much more.

Throughout data literacy month, we’ve shined a light on the importance of data literacy skills and how it impacts individuals and organizations. Equally as important is how to actually approach transformational data literacy programs and ensure they are successful. In this final episode of Data Literacy Month, we are unpacking how CBRE is upskilling over 3,000 of its employees on data literacy skills through a relevant, high-value learning program. Emily Hayward is the Data and Digital Change Manager at CBRE, a global leader in commercial real estate services and investment. Emily is a transformational leader with a track record of leading successful high-profile technology, data, and cultural transformations across both the public and private sectors through an ardent belief that change cannot be achieved without first winning people over. Throughout the episode, we talk about Emily’s approach to building CBRE’s learning program, effective change management, why it’s critical to secure executive sponsorship, and much more. Looking to build a data literacy program of your own? Check out DataCamp for Business: https://bit.ly/3r7BgsF

Understanding and interpreting data visualizations are one of the most important aspects of data literacy. When done well, data visualization ensures that stakeholders can quickly take away critical insights from data. Moreover, data visualization is often the best place to start when increasing organizational data literacy, as it’s often titled the “gateway drug” to more advanced data skills.Andy Cotgreave, Senior Data Evangelist at Tableau Software and co-author of The Big Book of Dashboards, joins the show to break down data visualization and storytelling, drawing from his 15-year career in the data space. Andy has spoken for events like SXSW, Visualized, and Tableau’s conferences and has inspired thousands of people to develop their data skills.In this episode, we discuss why data visualization skills are so essential, how data visualization increases organizational data literacy, the best practices for visual storytelling, and much more.This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness about Data Literacy throughout September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Data Literacy may be an important skill for everyone to have, but the level of need is always unique to each individual. Some may need advanced technical skills in machine learning algorithms, while others may just need to be able to understand the basics. Regardless of where anyone sits on the skills spectrum, the data community can help accelerate their careers. There’s no one who knows that better than Kate Strachnyi. Kate is the Founder and Community Manager at DATAcated, a company that is focused on bringing data professionals together and helping data companies reach their target audience through effective content strategies. Kate has created courses on data storytelling, dashboard and visualization best practices, and she is also the author of several books on data science, including a children’s book about data literacy. Through her professional accomplishments and her content efforts online, Kate has not only built a massive online following, she has also established herself as a leader in the data space. In this episode, we talk about best practices in data visualization, the importance of technical skills and soft skills for data professionals, how to build a personal brand and overcome Imposter Syndrome, how data literacy can make or break organizations, and much more. This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Data Literacy is increasingly becoming a skill that every role needs to have, regardless of whether their role a data-oriented or not. No one knows this better than Jordan Morrow, who is known as the Godfather of Data Literacy. Jordan is the VP and Head of Data Analytics at Brainstorm, Inc., and is the author of Be Data Literate: The Skills Everyone Needs to Succeed.Jordan has been a fierce advocate for data literacy throughout his career, including helping the United Nations understand and utilize data literacy effectively. Throughout the episode, we define data literacy, why organizations need data literacy in order to use data properly and drive business impact, how to increase organizational data literacy, and more. This episode of DataFramed is a part of DataCamp’s Data Literacy Month, where we raise awareness for Data Literacy throughout the month of September through webinars, workshops, and resources featuring thought leaders and subject matter experts that can help you build your data literacy, as well as your organization’s. For more information, visit: https://www.datacamp.com/data-literacy-month/for-teams

Taking inspiration from International Literacy Day on September 8, DataCamp is dedicating the whole month of September to raising awareness about Data Literacy. Throughout the month, we are featuring thought leaders and subject matter experts in order to get you Data Literacy, and we can’t wait for you to hear the exceptional guests we have lined up for you right here on DataFramed. Check out the full lineup of events.

Many times, data scientists can fall into the trap of resume-driven development. As in, learning the shiniest, most advanced technique available to them in an attempt to solve a business problem. However, this is not what a learning mindset should look like for data teams. As it turns out, taking a step back and focusing on the fundamentals and step-by-step iteration can be the key to growing as a data scientist, because when data teams develop a strong understanding of the problems and solutions lying underneath the surface, they will be able to wield their tools with complete mastery. Ella Hilal joins the show to share why operating from an always-learning mindset will open up the path to a true mastery and innovation for data teams. Ella is the VP of Data Science and Engineering for Commercial and Service Lines at Shopify, a global commerce leader that helps businesses of all size grow, market, and manage their retail operations. Recognized as a leading woman in Data science, Internet of things and Machine Learning, Ella has over 15 years of experience spanning multiple countries, and is an advocate for responsible innovation, women in tech, and STEM. In this episode, we talk about the biggest mistakes data scientists make when solving business problems, how to create cohesion between data teams and the broader organization, how to be an effective data leader that prioritizes their team’s growth, and how developing an always-learning mindset based on iteration, experimentation, and deep understanding of the problems needing to be solved can accelerate the growth of data teams.

Most companies experience the same pain point when working with data: it takes too long to get the right data to the right people. This creates a huge opportunity for data scientists to find innovative solutions to accelerate that process.One very effective method is to implement real-time data solutions that can increase business revenue and make it easier for anyone relying on the data to access the data they need, understand it, and make accurate decisions with it.George Trujillo joins the show to share how he believes real-time data has the potential to completely transform the way companies work with data. George is the Principal Data Strategist at DataStax, a tech company that helps businesses scale by mobilizing real-time data on a single, unified stack. With a career spanning 30 years and companies like Charles Schwab, Fidelity Investments, and Overstock.com, George is an expert in data-driven executive decision-making and tying data initiatives to tangible business value outcomes.In this episode, we talk about the real-world use cases of real-time analytics, why reducing data complexity is key to improving the customer experience, the common problems that slow data-driven decision-making, and how data practitioners can start implementing real-time data through small high-value analytical assets.

Machine learning models are often thought to be mainly utilized by large tech companies that run large and powerful models to accomplish a wide array of tasks. However, machine learning models are finding an increasing presence in edge devices such as smart watches. ML engineers are learning how to compress models and fit them into smaller and smaller devices while retaining accuracy, effectiveness, and efficiency. The goal is to empower domain experts in any industry around the world to effectively use machine learning models without having to become experts in the field themselves. Daniel Situnayake is the Founding TinyML Engineer and Head of Machine Learning at Edge Impulse, a leading development platform for embedded machine learning used by over 3,000 enterprises across more than 85,000 ML projects globally. Dan has over 10 years of experience as a software engineer, which includes companies like Google (where he worked on TensorFlow Lite) and Loopt, and co-founded Tiny Farms America’s first insect farming technology company. He wrote the book, "TinyML," and the forthcoming "AI at the Edge". Daniel joins the show to talk about his work with EdgeML, the biggest challenges facing the field of embedded machine learning, the potential use cases of machine learning models in edge devices, and the best tips for aspiring machine learning engineers and data science practitioners to get started with embedded machine learning.

Many machine learning practitioners dedicate most of their attention to creating and deploying models that solve business problems. However, what happens post-deployment? And how should data teams go about monitoring models in production? Hakim Elakhrass is the Co-Founder and CEO of NannyML, an open-source python library that allows users to estimate post-deployment model performance, detect data drift, and link data drift alerts back to model performance changes. Originally, Hakim started a machine learning consultancy with his NannyML co-founders, and the need for monitoring quickly arose, leading to the development of NannyML. Hakim joins the show to discuss post-deployment data science, the real-world use cases for tools like NannyML, the potentially catastrophic effects of unmonitored models in production, the most important skills for modern data scientists to cultivate, and more.

One of the biggest challenges facing the adoption of machine learning and AI in Data Science is understanding, interpreting, and explaining models and their outcomes to produce higher certainty, accountability, and fairness. Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python. For the last two decades, Serg has been at the confluence of the internet, application development, and analytics. Serg is a true polymath. Before his current role, he co-founded a search engine startup incubated by Harvard Innovation Labs, was the proud owner of a Bubble Tea shop, and more. Throughout the episode, Serg spoke about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more.

Anjali Samani, Director of Data Science & Data Intelligence at Salesforce, joins the show to discuss what it takes to become a mature data organization and how to build an impactful, diverse data team. As a data leader with over 15 years of experience, Anjali is an expert at assessing and deriving maximum value out of data, implementing long-term and short-term strategies that directly enable positive business outcomes, and how you can do the same. You will learn the hallmarks of a mature data organization, how to measure ROI on data initiatives, how Salesforce implements its data science function, and how you can utilize strong relationships to develop trust with internal stakeholders and your data team.

In 2020, OpenAI launched GPT-3, a large language AI model that is demonstrating the potential to radically change how we interact with software, and open up a completely new paradigm for cognitive software applications. Today’s episode features Sandra Kublik and Shubham Saboo, authors of GPT-3: Building Innovative NLP Products Using Large Language Models. We discuss what makes GPT-3 unique, transformative use-cases it has ushered in, the technology powering GPT-3, its risks and limitations, whether scaling models is the path to “Artificial General Intelligence”, and more. Announcement For the next seven days, DataCamp Premium and DataCamp for Teams are free. Gain free access by following going here. 

While leading a mature data science function is a challenge in its own right, building one from scratch at an organization can be just as, if not even more, difficult. As a data leader, you need to balance short-term goals with a long-term vision, translate technical expertise into business value, and develop strong communication skills and an internalized understanding of a business's values and goals in order to earn trust with key stakeholders and build the right team. Elettra Damaggio is no stranger to this process. Elettra is the Director for Global Data Science at StoneX, an institutional-grade financial services network that connects clients to the global markets ecosystem. Elettra has over 10 years of experience in machine learning, AI, and various roles within digital transformation and digital business growth. In this episode, she shares how data leaders can balance short-term wins with long-term goals, how to earn trust with stakeholders, major challenges when launching a data science function, and advice she has for new and aspiring data practitioners.

In pharmaceuticals, wrong decisions can not only cost a company revenue, but they can also cost people their lives. With stakes so high, it’s vital that pharmaceutical companies have robust systems and processes in place to accurately gather, analyze, and interpret data and turn it into actionable steps to solving health issues. Suman Giri is the Global Head of Data Science of the Human Health Division at Merck, a biopharmaceutical research company that works to develop innovative health solutions for both people and animals. Suman joins the show today to share how Merck is using data to improve organizational decision-making, medical research outcomes, and how data science is transforming the pharmaceutical industry at scale. He also shares some of the biggest challenges facing the industry right now and what new trends are on the horizon.

Building data science functions has become tables takes for many organizations today. However, before data science functions were needed, the finance function acted as the insights layer for many organizations over the past. This means that working in finance has become an effective entry point into data science function for professionals across all spectrums. Brian Richardi is the Head of Finance Data Science and Analytics at Stryker, a medical equipment manufacturing company based in Michigan, US. Brian brings over 14 years of global experience to the table. At Stryker, Brian leads a team of data scientists that use business data and machine learning to make predictions for optimization and automation. In this episode, Brian talks about his experience as a data science leader transitioning from Finance, how he utilizes collaboration and effective communication to drive value, how leads the data science finance function at Stryker, and what the future of data science looks like in the finance space, and more.

Democratizing data, and developing data culture in large enterprise organizations is an incredibly complex process that can seem overwhelming if you don’t know where to start. And today’s guest draws a clear path towards becoming data-driven. Meenal Iyer, Sr. Director for Data Science and Experimentation at Tailored Brands, Inc., has over 20 years of experience as a Data and Analytics strategist. She has built several data and analytics platforms and drives the enterprises she works with to be insights-driven. Meenal has also led data teams at various retail organizations, and as a wide variety of specialties in Data Science, including data literacy programs, data monetization, machine learning, enterprise data governance, and more. In this episode, Meenal shares her thorough, effective, and clear strategy for democratizing data successfully and how that helps create a successful data culture in large enterprises, and gives you the tools you need to do the same in your organization. [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

When many people talk about leading effective Data Science teams in large organizations, it’s easy for them to forget how much effort, intentionality, vision, and leadership are involved in the process. Glenn Hofmann, Chief Analytics Officer at New York Life Insurance, is no stranger to that work. With over 20 years of global leadership experience in data, analytics, and AI that spans the US, Germany, and South Africa, Glenn knows firsthand what it takes to build an effective data science function within a large organization. In this episode, we talk about how he built NeW York Life Insurance’s 50-person data science and AI function, how they utilize skillsets to offer different career paths for data scientists, building relationships across the organization, and so much more. [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

The healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience. Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, believes that despite challenges like these, there are massive opportunities for data science and machine learning to increase care quality, drive business objectives, diagnose diseases earlier, and ultimately save countless lives around the world. Curren has over 10 years of leadership experience across both the US and Europe and has led more than 20 successful data science product launches in the payer, provider, and pharmaceutical spaces. She also brings her background as a cognitive neuroscientist to data science, with research in neural networks, connectivity analysis, and more. [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today marks the last episode of our four-part DataFramed Careers Series on breaking into a data career. We’ve heard from Sadie St Lawrence, Nick Singh, and Khuyen Tran on best practices to adopt to help you land a data science interview. But what about the interview itself? Today’s guest, Jay Feng, joins the show to break down all the most important things you need to know about interviewing for data science roles. Jay is the co-founder of Interview Query, which helps data scientists, machine learning engineers, and other data professionals prepare for their dream jobs. Throughout the episode, we discuss The anatomy of data science interviews Biggest misconceptions and mistakes candidates make during interviews The importance of showcasing communication ability, business acumen, and technical intuition in the interview How to negotiate for the best salary possible [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today is the third episode of this four-part DataFramed Careers series being published every day this week on building a career in data. We’ve heard from Nick Singh on the importance of portfolio projects, as well as the distinction between content-based and coding-based portfolio projects. When looking to get started with content-based projects, how do you move forward with getting yourself out there and sharing the work despite being a relative beginner in the field?Today’s guest tackles exactly this subject. Khuyen Tran is a developer advocate at prefect and a prolific data science writer. She is the author of the book “Efficient Python Tricks and Tools for Data Scientists” and has written 100s of blog-articles and tutorials on key data science topics, amassing thousands of followers across platforms. Her writing has been key to accelerating here data career opportunities. Throughout the episode, we discuss: How content creation accelerates the careers of aspiring practitioners The content creation process How to combat imposter syndrome What makes content useful Advice and feedback for aspiring data science writers   Resources mentioned in the episode: Analyze and Visualize URLs with Network Graph Show Your Work by Austin Cloud Mastery by Robert Greene Deep Questions with Cal Newport Podcast   [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today marks the second episode in our DataFramed Careers Series. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of landing a data role in 2022. In the first episode of the series, Sadie discussed at great length the importance of having a solid data science portfolio to land a role in data. But what makes a great data science portfolio? Nick Singh, co-author of Acing the Data Science Interview, joins the show to share everything you need to know to create high-quality, thorough portfolio projects. Throughout the episode, we discuss How portfolio projects build experience Who should be focusing on portfolio projects The different types of portfolio projects Biggest pitfalls when creating portfolio projects How to get noticed with your portfolio projects Concrete examples of great portfolio projects  [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Today is the start of a four-day careers series covering breaking into data science in 2022. With so so much demand for data jobs today, we wanted to demystify the ins and outs of accelerating a career in data. In this series, we will interview a diverse range of thought leaders and experts on the different aspects of standing out from the crowd in the job hunt. Our first guest in the DataFramed Careers Series is Sadie St. Lawrence. Sadie St Lawrence is the Founder and CEO of Women in Data, the #1 Community for Women in AI and Tech. Women in Data is a community of over 20,000 individuals and has representation in 17 countries and 50 cities. She has trained over 350,000 people in data science and is the course developer for the Machine Learning Certification for UC Davis. In addition, she serves on multiple start-up boards, and is the host of the Data Bytes podcast. Sadie joins the show to talk about her career journey in data science and shares the best lessons she has learned in launching data careers. Throughout the episode, we discuss The different types of data career paths available How to break into your data science career How to build strong mentor/mentee relationships Best practices to stand out in a competitive industry Building a strong resume and standing out from the crowd  [Announcement] Join us for DataCamp Radar, our digital summit on June 23rd. During this summit, a variety of experts from different backgrounds will be discussing everything related to the future of careers in data. Whether you're recruiting for data roles or looking to build a career in data, there’s definitely something for you. Seats are limited, and registration is free, so secure your spot today on https://events.datacamp.com/radar/

Introducing the DataFramed Careers Series. Over the past year hosting the DataFramed podcast, we've had the incredible privilege of having biweekly conversations with data leaders at the forefront of the data revolution. This has led to fascinating conversations on the future of the modern data stack, the future of data skills, and how to build organizational data literacy.  However, as the DataFramed podcast grows, we want to be able to provide the data science community across the spectrum from practitioners to leaders, with distilled insights that will help them manoeuvre their careers effectively. And we want to do that more often.  This is why we’re excited to announce the launch of a four-day DataFramed Careers Series. Throughout next week, we will interview four different thought leaders and experts about what it takes to break into data science in 2022, best practices to stand out from the crowd, building a brand in data science, and more. Moreover, this episode series will mark DataFramed’s transition from biweekly to weekly. Starting Monday the 30th of May, DataFramed will become a weekly podcast. For next week’s DataFramed Careers Series, we’ll be covering the ins and outs of building a career in data, and the different aspects of standing out from the crowd during the job hunt. We’ll be hearing from Sadie St Lawrence, CEO and Founder of Women in Data on what it takes to launch a data career in 2022. Nick Singh, Co-author of Ace the Data Science Interview and 2nd time guest of DataFramed will join us to discuss what makes a great data science portfolio project. Khuyen Tran, Developer Advocate at Prefect on will outline how writing can accelerate a data career, and Jay Feng, CEO of Interview Query will join us to provide tips and frameworks on acing the data science interview. For future DataFramed episodes, we’ll definitely still cover the different aspects of building a data-driven organization, cover the latest advancements in data science, building data careers, and more. So expect more varied guests, topics, and more specials series like this one in the future.

Data literacy at any organization takes buy-in from all levels of the company, from C-suite leaders all the way to customer-facing team members. But how do you get that buy-in, build a team around data literacy, and transform the way your company works with data? Today’s guest, Megan Brown, Director of Data Literacy and Knowledge Management at Starbucks, discusses what they have done to forge data culture and data literacy at Starbucks. Throughout the episode, we discuss How to increase data literacy in an organization How to secure executive sponsorship for data initiatives The importance of user experience research in building data literacy  Balancing short-term business needs with long-term strategic upskilling Humanizing machine learning and AI within the organization 

Diversity in both skillset and experience are at the core of high-impact data teams, but how can you take your data team’s impact to the next level with subject matter expertise, attention to user experience, and mentorship? Today’s guest, Dan Kellet, Chief Data Officer at Capital One UK, joins us to discuss how he scaled Capital One’s data team. Throughout the episode, we discuss: The hallmarks of a high-impact data team The importance of skills and background diversity when building great data teams The importance of UX skills when developing data products The specific challenges of leading data teams in financial services

As data volumes grow and become ever-more complex, the role of the data analyst has never been more important. At the disposal of the modern data analyst, are tools that reduce time to insight, and increase collaboration. However, as the tools of a data analyst evolve, so do the skills.  Today’s guest, Peter Fishman, Co-Founder at Mozart Data, speaks to this exact notion.  Join us as we discuss: Defining a data-driven organization & main challenges Breaking down the modern data stack & what it means What makes a great data analyst How data analysts can develop deep subject matter expertise in the areas they serve Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player.

When you hear the term-digital first, you might think about tech, platforms and data.  But digital transformation succeeds when you put people first. Gathering and analyzing data, then using it to provide the customer value and an unparalleled experience, is vital for an organization’s success. Today’s guest, Bhavin Patel, Director o f Analytics and Innovation at J&J joins the show to share why people are the most important component to digital transformation. Join us as we discuss: Why you need to put people first The importance of customer value and experience Why digital transformation is an ongoing process, not an end-state  Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player.

The data journey is a slow painstaking process. But knowing where to start and the areas to focus on can help any organization reach its goals faster. Today’s guest, Vijay Yadav, Director of Quantitative Sciences & Head of Data Science at the Center for Mathematical Sciences at Merck, explains the 6 key elements of data strategy, complete with advice on how to navigate each. Join us as we discuss: The different components of a data strategy Shifting mindset within the C-Suite Structuring the operating model Enabling people to work with data at scale Most effective tactics to kickstart a community around data science Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player.

It’s no secret that data science jobs are on the rise; but data skills across the board are rising — leading to what today’s guest calls “hybrid jobs.” This will require a paradigm shift in how we think about jobs and skills.   Today’s guest, Matt Sigelman, President of The Burning Glass Institute & Chairman of Emsi Burning Glass, talks about the difficulties of connecting companies with top talent, the hybridization of many positions, and how to position yourself in the ever-changing market.  Join us as we discuss: The methodology of using data science on the labor market The demand for data skills & how they’re evolving Blending skills to get ahead in the job market & the rise of subskills How educational institutions can prepare students for hybridization  Advice to the audience on how to structure their approach to skill acquisition  Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player.

Throughout the middle east, efforts are underway to build smart cities from the ground up. But to create a modern, intelligently-designed city, you first need to lay a solid foundation. And the strongest foundation you can build a smart city upon is data. In today’s episode, we speak with Kaveh Vessali, Digital, Data & AI Leader, PwC Middle East, about the intersection between data and public policy and the many exciting insights he’s gained from his role delivering smart cities and data transformation projects within the public sector in the middle east.  Join us as we discuss: The important role data plays in shaping public policy  What goes into designing a smart city The change management skills vital for successful digital transformation  Data ethics and the importance of transparency   Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn.  Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player. 

When most people hear digital transformation, it’s almost always the technology that first springs to mind. That’s a mistake. You can have the most sophisticated tech stack in the world, but if you don't build your organization’s data culture, your digital transformation efforts will be for naught. Today’s guest, Mai AlOwaish, Chief Data Officer at Gulf Bank, knows this better than anyone. As the first female CDO in Kuwait, she’s on a mission to ensure everyone at Gulf Bank becomes an expert in the data they use every day. Join us as we discuss: Why data and people are more important than technology for digital transformationThe pioneering Data Ambassador program Mai spearheaded at Gulf BankThe importance of diversity in data science and technology overall Find every episode of DataFramed on Apple, Spotify, and more. Find us on our website and join the conversation on LinkedIn. Listening on a desktop and can’t see the links? Just search for DataFramed in your favorite podcast player. 

As we enter the new year—it seems like we’re telescoping into the future of work. Companies embracing remote work, the great resignation putting pressure on teams to create more fulfilling roles—signals an expanding opportunity for applicants to find their dream roles in data science, but also for hiring managers to create awesome candidate experiences.  Today’s guests, Nick Singh, and Kevin Huo, authors of Ace The Data Science Interview, discuss how aspiring data scientists and data scientists can stand out from their crowd—and what hiring managers need to change to win over talent today.  Join us as we discuss: How to wow recruiters and hiring managers with your resumeThe type of skills aspiring data scientists need to show on the job huntThe value of direct email over job listingsWhat recruiters and hiring managers need to change in an evolving job market Relevant links from the interview: Ace the Data Science InterviewFollow Nick Singh on LinkedInFollow Kevin Huo on LinkedInNoah Gift’s Appearance on DataFramedSign up to gain early access to gain DataCamp Talent—DataCamp’s portal for data science jobs

In this episode of DataFramed, we speak with Vishnu V Ram, VP of Data Science and Engineering at Credit Karma about how data science is being leveraged to increase financial inclusion. Throughout the episode, Vishnu discusses his background, Credit Karma’s mission, how data science is being used at Credit Karma to lower the barrier to entry for financial products, how he managed a data team through rapid growth, transitioning to Google Cloud, exciting trends in data science, and more.  Relevant links from the interview: You can now learn data science with your team for free—try out DataCamp Professional with our 14-day free trial. Data roles at Credit KarmaCredit Karma’s mission

In this episode of DataFramed, we speak with Andy Cotgreave, Technical Evangelist at Tableau about the role of data storytelling when driving change with analytics, and the importance of the analyst role within a data-driven organization. Throughout the episode, Andy discusses his background, the skills every analyst should know to equip organizations with better data-driven decision making, his best practices for data storytelling, how he thinks about data literacy and ways to spread it within the organization, the importance of community when creating a data-driven organization, and more. Relevant links from the interview: We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out our upcoming webinar with AndyCheck out Andy's bookBecome a Tableau expert

In this episode of DataFramed, we speak with Brian Campbell, Engineering Manager at Lucid Software about managing data science projects effectively and harnessing the power of collaboration. Throughout the episode, Brian discusses his background, how data leaders can become better collaborators, data science project management best practices, the type of collaborators data teams should seek out, the latest innovations in the data engineering tooling space, and more. Relevant links from the interview: We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyLucid’s Tech Blog

In this episode of DataFramed, we speak with Shameek Kundu, former group CDO at Standard Chartered Bank, and Chief Strategy Officer & Head of Financial Services at TruEra Inc about Scaling AI Adoption throughout financial services. Throughout the episode, Shameek discusses his background, the state of data transformation in financial services, the depth vs breadth of machine learning operationalization in financial services today, the challenges standing in the way of scalable AI adoption in the industry, the importance of data literacy, the trust and responsibility challenge of AI, the future of data science in financial services, and more. Relevant links from the interview: We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out TruEra in actionBank of England Report: The impact of Covid on machine learning and data science in UK BankingMIT Tech Review — Hundreds of AI tools have been built to catch covid. None of them helped

In this episode of DataFramed, we speak with Syafri Bahar, VP of Data Science at Gojek about building high-performing data teams, and how data science is central to Gojek’s success.  Throughout the episode, Syafri discusses his background, the hallmarks of a high-performance data team, how he measures the ROI on data activities, the skills needed in every successful data team, what is the best organizational model for data mature organizations, how Covid-19 affected Gojek’s data teams, his thoughts on data literacy and governance, future trends in data science and AI, and why data scientists should sharpen their maths and machine learning skills in an age of increasing automation.  Relevant links from the interview: We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyGojek’s Data Blog

In this episode of DataFramed, we speak with Noah Gift, founder of Pragmatic AI Labs and prolific author about operationalizing machine learning in organizations and his new book Practical MLOPs.  Throughout the episode, Noah discusses his background, his philosophy around pragmatic AI, the differences between data science in academia and the real world, how data scientists can become more action-oriented by creating solutions that solve real-world problems, the importance of dev-ops, his most recent book on the practical guide to MLOps, how data science can be compared to Brazilian jiu-jitsu, what data scientists should learn to scale the amount of value they deliver, his thoughts on auto-ml and automation, and more.  Relevant links from the interview: We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyUnsettled: What Climate Science Tells Us, What It Doesn't, and Why It MattersCheck out Noah's booksCheck out Noah's course on DataCampConnect with Noah on LinkedInGain access to DataCamp's full course library at a discount!

In this episode of DataFramed, we speak with Rick Scavetta and Boyan Angelov about their new book, Python and R for the Modern Data Scientist: The Best of Both Worlds, and how it dawns the start of a new bilingual data science community.   Throughout the episode, Rick and Boyan discuss the history of Python and R, what led them to write the book, how Python and R can be interoperable, the advantages of each language and where to use it, how beginner data scientists should think about learning programming languages, how experienced data scientists can take it to the next level by learning a language they’re not necessarily comfortable with, and more.  Relevant links from the interview: We’d love your feedback! Let us know which topics you’d like us to cover and what you think of DataFramed by answering this 30-second surveyCheck out Rick and Boyan’s bookCheck out Rick’s courses on DataCampCheck out Boyan's other booksConnect with Rick on LinkedInConnect with Boyan on LinkedIn

In this episode of DataFramed, we speak with Brent Dykes, Senior Director of Insights & Data Storytelling at Blast Analytics and author of Effective Data Storytelling: How to Turn Insights into Action on how data storytelling is shaping the analytics space.  Throughout the episode, Brent talks about his background, what made him write a book on effective data storytelling, how data storytelling is often misinterpreted and misused, the psychology of storytelling and how humans are shaped to resonate with it, the role of empathy when creating data stories, the blueprint of a successful data story, what data scientists can do to become better data storytellers, the future of augmented analytics and data storytelling, and more.  Relevant links from the interview: Connect with Brent on LinkedInRegister for Brent's Webinar on DataCampCheck out Brent's Book

In this episode of DataFramed, Adel speaks with Maria Luciana Axente, Responsible AI and AI for Good Lead at PwC UK on the state and future of responsible AI.Throughout the episode, Maria talks about her background, the differences & intersections between "AI ethics" and "Responsible AI", the state of responsible AI adoption within organizations, the link between responsible AI and organizational culture, what data scientists can do today to ensure they're part of their organization's responsible AI journey, and more. Relevant links from the interview: Connect with Maria on LinkedInKate Crawford's Atlas of AI9 Ethical AI Principles for Organizations to FollowPwC's Responsible AI ToolkitRead our Data Literacy for Responsible AI White Paper

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs,  an AI Observability company on a mission to build the interface between AI and human operators. Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more.  Relevant links from the interview: Connect with Alessya on LinkedInAndrew Ng on the important of being data-centricJoe Reis on the data culture and all things datawhylogs: the standard for data logging — please send you feedback, contribute, help us build integrations into your favorite data tools and extend the concept of logging to new data types. Join the effort of building a new open standard for data logging!Try the WhyLabs platform

In this episode of DataFramed, Adel speaks with Sudaman Thoppan Mohanchandralal, Regional Chief Data, and Analytics Officer at Allianz Benelux, on the importance of building data cultures and his experiences operationalizing data culture transformation programs.Throughout the episode, Sudaman talks about his background, the Chief Data Officer’s mandate and how it has evolved over the years, how organizations should prioritize building data cultures, the science behind culture change, the importance of executive data literacy when scaling value from data, and more. Relevant links from the interview: Connect with Sudaman on LinkedInCheck out Sudaman’s Webinar on DataCampWhy Data Culture Matters

In this episode of DataFramed, Adel speaks with Elad Cohen, VP of Data Science and Research at Riskified on how data science is being used to combat fraud in eCommerce.Throughout the episode, Elad talks about his background, the plethora of data science use-cases in eCommerce, how Riskified builds state-of-the-art fraud detection models, common pitfalls data teams face, his best practices gaining organizational buy-in for data projects, how data scientists should focus on value, whether they should have engineering skills, and more. Relevant links from the interview: Connect with Elad on LinkedInRegister for our upcoming webinarsHow Riskified chooses what to research

In this episode of DataFramed, Adel speaks with Barr Moses, CEO, and co-founder of Monte Carlo on the importance of data quality and how data observability creates trust in data throughout the organization.  Throughout the episode, Barr talks about her background, the state of data-driven organizations and what it means to be data-driven, the data maturity of organizations, the importance of data quality, what data observability is, and why we’ll hear about it more often in the future. She also covers the state of data infrastructure, data meshes, and more.  Relevant links from the interview: Connect with Barr on LinkedInLearn more about data meshesCheck out the Monte Carlo blogDataCamp's Guide to Organizational Data Maturity

In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today. Throughout the episode, Sergey deep-dives into his background, the various projects he’s been involved with throughout his career, the most exciting advances he’s seen in the data science space, the largest challenges facing data teams today, best practices democratizing data, the importance of learning SQL, and more.  Relevant links from the interview: Connect with Sergey on LinkedInCheck out Sergey’s course on DataCampLearn more about AirflowLearn more about PySparkLearn more about SQL More resources from DataCamp Upskill your team with DataCampOur Guide on Open Source Software in Data ScienceYour Organization’s Guide to Data Maturity

In this episode of DataFramed, Adel speaks with Dan Becker, CEO of decision.ai and founder of Kaggle Learn on the intersection of decision sciences and AI, and best practices when aligning machine learning to business value. Throughout the episode, Dan deep-dives into his background, how he reached the top of a Kaggle competition, the difference between machine learning in a Kaggle competition and the real world, the role of empathy when aligning machine learning to business value, the importance of decisions sciences when maximizing the value of machine learning in production, and more.  Links: Follow Dan on TwitterFollow Dan on LinkedInWhat 70% of data science learners do wrongCheck out Dan’s course on DataCampdecision.aiDan’s climate dashboard

In this episode of DataFramed, Adel speaks with Amen Ra Mashariki, principal scientist at Nvidia and the former Chief Analytics Officer of the City of New York on how data science is done in government agencies, and how it's driving smarter cities all around us.  Throughout the episode, Amen deep-dives into the use-cases he worked on to make the city of New York smarter, how data science allows cities to become more reactive and proactive, the unique challenges of scaling data science in a government setting, the friction between providing value and data privacy and ethics, the state of data literacy in government, and more.  Links from the interview: Follow Amen on LinkedInFollow Amen on TwitterThe New York City Business AtlasHurricane Sandy FEMA After-Action ReportData Drills

We are super excited to be relaunching the DataFramed podcast. In this iteration of DataFramed, Adel Nehme, a data science educator at DataCamp, will uncover the latest thinking on all things data and how it’s impacting organizations through biweekly (once every two weeks) interviews and conversations with data experts from across the world.  Check out this snippet for a preview of what’s to come and for a short chat with DataCamp’s CEO Jonathan Cornelissen on where he thinks data science is headed and the major challenges facing data teams today.  Links: For the rest of April, get free access to DataCamp.Get involved with DataCamp Donates

Before the COVID-19 crisis, we were already acutely aware of the need for a broader conversation around data privacy: look no further than the Snowden revelations, Cambridge Analytica, the New York Times Privacy Project, the General Data Protection Regulation (GDPR) in Europe, and the California Consumer Privacy Act (CCPA). In the age of COVID-19, these issues are far more acute. We also know that governments and businesses exploit crises to consolidate and rearrange power, claiming that citizens need to give up privacy for the sake of security. But is this tradeoff a false dichotomy? And what type of tools are being developed to help us through this crisis? In this episode, Katharine Jarmul, Head of Product at Cape Privacy, a company building systems to leverage secure, privacy-preserving machine learning and collaborative data science, will discuss all this and more, in conversation with Dr. Hugo Bowne-Anderson, data scientist and educator at DataCamp.Links from the show FROM THE INTERVIEW Katharine on TwitterKatharine on LinkedInContact Tracing in the Real World (By Ross Anderson)The Price of the Coronavirus Pandemic (By Nick Paumgarten)Do We Need to Give Up Privacy to Fight the Coronavirus? (By Julia Angwin)Introducing the Principles of Equitable Disaster Response (By Greg Bloom)Cybersecurity During COVID-19 ( By Bruce Schneier)

This week, Hugo speaks with Sean Law about data science research and development at TD Ameritrade. Sean’s work on the Exploration team uses cutting edge theories and tools to build proofs of concept. At TD Ameritrade they think about a wide array of questions from conversational agents that can help customers quickly get to information that they need and going beyond chatbots. They use modern time series analysis and more advanced techniques like recurrent neural networks to predict the next time a customer might call and what they might be calling about, as well as helping investors leverage alternative data sets and make more informed decisions. What does this proof of concept work on the edge of data science look like at TD Ameritrade and how does it differ from building prototypes and products? And How does exploration differ from production? Stick around to find out. LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Sean on TwitterSean's WebsiteTD Ameritrade Careers PagePyData Ann Arbor MeetupPyData Ann Arbor YouTube Channel (Videos)TDA Github Account (Time Series Pattern Matching repo to be open sourced in the coming months)Aura Shows Human Fingerprint on Global Air Quality FROM THE SEGMENTS Guidelines for A/B Testing (with Emily Robinson ~19:20) Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson) Data Science Best Practices (with Ben Skrainka ~34:50) Debugging (By David J. Agans)Basic Debugging With GDB (By Ben Skrainka)Sneaky Bugs and How to Find Them (with git bisect) (By Wiktor Czajkowski)Good logging practice in Python (By Victor Lin) Original music and sounds by The Sticks.

This week, Hugo speaks with Debbie Berebichez about the importance of critical thinking in data science. Debbie is a physicist, TV host and data scientist and is currently the Chief Data Scientist at Metis in NY.In a world and a professional space plagued by buzz terms like AI, big data, deep learning, and neural networks, conversations around skill sets and less than productive programming language wars, what has happened to critical thinking in data science and data thinking in general? What type of critical thinking skills are even necessary as data science, AI and machine learning become even more present in all of our lives and how spread out do they need to be across organizations and society? Listen to find out!LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Debbie on TwitterDebbie's WebsiteDebbie Berebichez- Media Reel (Video)Deborah Berebichez' Keynote at Grace Hopper Celebration 2017 (Video)Debbie Berebichez on Perseverance and Paying it Forward (Video)Things about the Future and the Future of Things (By Debbie Berebichez, Video) FROM THE SEGMENTS Data Science tools for getting stuff done and giving it to the world (with Jared Lander ~21:55) Lander Analytics WebsiteDocker Websiteplumber Website Statistical Distributions and their Stories (with Justin Bois ~39:30) Probability distributions and their stories (By Justin Bois)The History of Statistics (By Stephen M. Stigler)The Evolution of the Normal Distribution (By Saul Stahl) Original music and sounds by The Sticks.

This week, Hugo will be speaking with Skipper Seabold about the current and looming credibility crisis in data science. Skipper is Director of Data Science at Civis Analytics, a data science technology and solutions company, and also the creator of the statsmodels package for statistical modeling and computing in python. Skipper is also a data scientist with a beard bigger than Hugo's. They’re going to be talking about how data science is facing a credibility crisis that is manifesting itself in different ways in different industries, how and why expectations aren’t met and many stakeholders are disillusioned. You’ll see that if the crisis isn’t prevented, the data science labor market may cease to be a seller’s market and we’ll have big missed opportunities. But this isn’t an episode of Black Mirror so they’ll also discuss how to avoid the crisis, taking detours through the role of randomized control trials in data science, the rise of methods borrowed from econometrics and how to set realistic expectations around what data science can and can’t do.LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Skipper on TwitterSkipper on GithubWhat's the Science in Data Science? (Video by Skipper Seabold)The Credibility Revolution in Empirical Economics: How Better Research Design Is Taking the Con out of Econometrics (By Joshua D. Angrist & Jörn-Steffen Pischke, American Economic Association)Project Management for the Unofficial Project Manager: A FranklinCovey Title (By Kory Kogon)Courtyard by Marriott Designing a Hotel Facility with Consumer-Based Marketing Models (Jerry Wind et al., The Institute of Management Sciences)Statsmodels's Documentation FROM THE SEGMENTS Guidelines for A/B Testing (with Emily Robinson ~15:48 & ~35:20) Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson) Original music and sounds by The Sticks.

This week, Hugo speaks with Noemi Derzsy, a Senior Inventive Scientist at AT&T Labs within the Data Science and AI Research organization, where she does lots of science with lots of data. They’ll be talking about her work at AT&T Labs Research, the mission of which is to look beyond today’s technology solutions to invent disruptive technologies that meet future needs. AT&T Labs works on a multitude of projects, from product development at AT&T, to how to combat bias and fairness issues in targeted advertising and creating drones for cell tower inspection research that leverages AI, ML and video analytics. They’ll be talking about some of the work Noemi does, from characterizing human mobility from cellular network data to characterizing their mobile network to analyze how its topology compares to other real social networks reported to understanding tv viewership, and how engaged people are in different shows. They’ll discuss what the future of data science looks like, whether it will even be around in 2029 and what types of skills would help you land a job in a place like AT&T Labs.LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Noemi on TwitterNoemi's WebsiteHuman Mobility Characterization from Cellular Network Data (By Richard Becker et al., Communications of the ACM)AT&T Labs Research WebsiteNASA Datanauts WebsiteOpen NASA Website FROM THE SEGMENTS Guidelines for A/B Testing (with Emily Robinson ~18:23 & ~36:38) Testing multiple statistical hypotheses resulted in spurious associations: a study of astrological signs and health (By Peter C. Austin et al., Journal of Clinical Epidemiology)From Infrastructure to Culture: A/B Testing Challenges in Large Scale Social Networks (By Ya Xu et al., LinkedIn Corp)Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson) Original music and sounds by The Sticks.

This week, Hugo speaks with Chris Albon about getting your first data science job. Chris is a Data Scientist at Devoted Health, where he uses data science and machine learning to help fix America's healthcare system. Chris is also doing a lot of hiring at Devoted and that’s why he’s so excited today to talk about how to get your first data science job. You may know Chris as co-host of the podcast Partially Derivative, from his educational resources such as his blog and machine learning flashcards or as one of the funniest data scientists on Twitter.LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Chris on TwitterChris's WebsiteDevoted WebsiteMachine Learning Flashcards (By Chris Albon)Machine Learning with Python Cookbook (By Chris Albon) FROM THE SEGMENTS Guidelines for A/B Testing (with Emily Robinson ~26:50) Guidelines for A/B Testing (By Emily Robinson)10 Guidelines for A/B Testing Slides (By Emily Robinson) Original music and sounds by The Sticks.

This week, Hugo speaks with Reshama Shaikh, about women in machine learning and data science, inclusivity and diversity more generally and how being intentional in what you do is essential. Reshama, a freelance data scientist and statistician, is also an organizer of the meetup groups Women in Machine Learning & Data Science (otherwise known  as WiMLDS) and PyLadies. She has organized WiMLDS for 4 years and is a Board Member. They’ll discuss her work at WiMLDS and what you can do to support and promote women and gender minorities in data science. They’ll also delve into why women are flourishing in the R community but lagging in Python and discuss more generally how NUMFOCUS thinks about diversity and inclusion, including their code of conduct. All this and more.LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Reshama’s BlogReshama on TwitterList of Relevant Conferences (and Code of Conduct info)NYC PyLadies meetupCode of Conduct for NeurIPS and Other Stem OrganizationsNumFOCUS Diversity & Inclusion in Scientific Computing (DISC)NumFOCUS DISCOVER Cookbook (for inclusive events)fastai deep learning notes WiMLDS (Women in Machine Learning and Data Science) NYC WiMLDS meetupTo start a WiMLDS chapter: email info@wimlds.org and more info at our starter kit.WiMLDS WebsiteGlobal List of WiMLDS Meetup ChaptersWiMLDS Paris: They run their meetups in English, so knowledge of French is not required.   FROM THE SEGMENTS DataCamp User Stories (with David Sudolsky ~17:27 & ~31:50) Boldr Website Original music and sounds by The Sticks.

This week, Hugo speaks with Marco Blume, Trading Director at Pinnacle Sports. Marco and Hugo will talk about the role of data science in large-scale bets and bookmaking, how Marco is training an army of data scientists and much more. At Pinnacle, Marco uses tight risk-management built on cutting-edge models to provide bets not only on sports but on questions such as who will be the next pope? Who will be the world hot dog eating champion, who will land on mars first and who will be on the iron throne at the end of game of thrones. They’ll discuss the relations between risk management and uncertainty, how great forecasters are necessarily good at updating their predictions in the light of new data and evidence, how you can model this using Bayesian inference and the future of biometric sensing in sports betting. And, as always, much, much more.LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Pinnacle WebsiteTraining an army of new data scientists (Presentation by Marco Blume) FROM THE SEGMENTS Data Science Best Practices (with Ben Skrainka ~16:40) Python Debugging With Pdb (By Nathan Jennings)pdb Tutorial (Github)The Visual Python Debugger for Jupyter Notebooks You’ve Always Wanted (By David Taieb)Debugging with RStudio (By Jonathan McPherson)Basics of Debugging Statistical Distributions and their Stories (with Justin Bois at ~36:00) Justin's Website at CaltechProbability distributions and their stories (By Justin Bois) Original music and sounds by The Sticks.

This week on DataFramed, the DataCamp podcast, Hugo speaks with Gabriel Straub, the Head of Data Science and Architecture at the BBC, where his role is to help make the organization more data informed and to make it easier for product teams to build data and machine learning powered products. They’ll be talking about data science and machine learning at the BBC and how they can impact content discoverability, understanding content, putting the right stuff in front of people, how Gabriel and his team develop broader data science & machine learning architecture to make sure best practices are adopted and what it means to apply machine learning in a sensible way. How does the BBC think about incorporating data science into its business, which has been around since 1922 and historically been at the forefront of technological innovation such as in radio and television? Listen to find out!LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Gabriel Straub: It's bigger on the inside (Video)BBC datalab FROM THE SEGMENTS DataCamp User Stories (with Krittika Patil ~16:10 & ~38:12) Kespry (Drone Aerial Intelligence for Industry) Original music and sounds by The Sticks.

This week Hugo speaks with Dr. Brandeis Marshall, about people of color and under-represented groups in data science. They’ll talk about the biggest barriers to entry for people of color, initiatives that currently exist and what we as a community can do to be as diverse and inclusive as possible. Brandeis is an Associate Professor of Computer Science at Spelman College. Her interdisciplinary research lies in the areas of information retrieval, data science, and social media. Other research includes the BlackTwitter Project, which blends data analytics, social impact and race as a lens to understanding cultural sentiments. Brandeis is involved in a number of projects, workshops, and organizations that support data literacy and understanding, share best data practices and broaden participation in data science. LINKS FROM THE SHOW DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on DataFramed?) FROM THE INTERVIEW Brandeis on TwitterThe BlackTwitter ProjectThe Impact of Live Tweeting on Social Movements (By Brandeis Marshall, Takeria Blunt, Tayloir Thompson)EvergreenLP: Using a social network as a learning platform (By Brandeis Marshall, Jaye Nias, Tayloir Thompson, Takeria Blunt)Journal of Computing Sciences in Colleges (By Brandeis Marshall)DSX (Data Science eXtension Faculty development and undergraduate instruction in data science) African American Women Computer Science PhDs500 Women ScientistsBlack in AIWomen in Machine Learning FROM THE SEGMENTS What Data Scientists Really Do (with Hugo Bowne-Anderson & Emily Robinson ~21:30 & ~41:40) What Data Scientists Really Do, According to 35 Data Scientists (Harvard Business Review article by Hugo Bowne-Anderson)What Data Scientists Really Do, According to 50 Data Scientists (Slides from a talk by Hugo Bowne-Anderson) Original music and sounds by The Sticks.

In episode 50, our Season 1, 2018 finale of DataFramed, the DataCamp podcast, Hugo speaks with Cathy O’Neil, data scientist, investigative journalist, consultant, algorithmic auditor and author of the critically acclaimed book Weapons of Math Destruction. Cathy and Hugo discuss the ingredients that make up weapons of math destruction, which are algorithms and models that are important in society, secret and harmful, from models that decide whether you keep your job, a credit card or insurance to algorithms that decide how we’re policed, sentenced to prison or given parole? Cathy and Hugo discuss the current lack of fairness in artificial intelligence, how societal biases are perpetuated by algorithms and how both transparency and auditability of algorithms will be necessary for a fairer future. What does this mean in practice? Tune in to find out. As Cathy says, “Fairness is a statistical concept. It's a notion that we need to understand at an aggregate level.” And, moreover, “data science doesn't just predict the future. It causes the future.”LINKS FROM THE SHOW DATAFRAMED SURVEY DataFramed Survey (take it so that we can make an even better podcast for you) DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on Season 2?) FROM THE INTERVIEW Cathy on TwitterCathy's Blog MathbabeWeapons of Math Destruction: How big data increases inequality and threatens democracy by Cathy O'NeilCathy's Opinion Column, Bloomberg Doing Data Science (By Cathy O'Neil and Rachel Schutt)Cathy O'Neil & Hanna Gunn's "Ethical Matrix" paper coming soon. FROM THE SEGMENTS Data Science Best Practices (with Heather Nolis ~20:30) Using docker to deploy an R plumber API (By Jonathan Nolis and Heather Nolis)Enterprise Web Services with Neural Networks Using R and TensorFlow (By Jonathan Nolis and Heather Nolis) Data Science Best Practices (with Ben Skrainka ~39:35) The Clean Coder Blog (By Robert C. Martin)James Shore’s blog post on Red, Green, RefactorJeff Knupp’s Python Unittesting tutorial (general unit tests in Python)John Myles White’s Intro to Unit Testing in R Original music and sounds by The Sticks.

Hugo speaks with Wes McKinney, creator of the pandas project for data analysis tools in Python and author of Python for Data Analysis, among many other things. Wes and Hugo talk about data science tool building, what it took to get pandas off the ground and how he approaches building “human interfaces to data” to make individuals more productive. On top of this, they’ll talk about the future of data science tooling, including the Apache arrow project and how it can facilitate this future, the importance of DataFrames that are portable between programming languages and building tools that facilitate data analysis work in the big data limit. Pandas initially arose from Wes noticing that people were nowhere near as productive as they could be due to lack of tooling & the projects he’s working on today, which they’ll discuss, arise from the same place and present a bold vision for the future.LINKS FROM THE SHOWDATAFRAMED SURVEY DataFramed Survey (take it so that we can make an even better podcast for you) DATAFRAMED GUEST SUGGESTIONS DataFramed Guest Suggestions (who do you want to hear on Season 2?) FROM THE INTERVIEW Wes on TwitterRoads and Bridges: The Unseen Labor Behind Our Digital Infrastructure by Nadia Eghbalpandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.Ursa Labs FROM THE SEGMENTS Data Science Best Practices (with Ben Skrainka ~17:10) To Explain or To Predict? (By Galit Shmueli)Statistical Modeling: The Two Cultures (By Leo Breiman)The Book of Why (By Judea Pearl & Dana Mackenzie) Studies in Interpretability (with Peadar Coyle at ~39:00) Modelling Loss Curves in Insurance with RStan (By Mick Cooney)Lime: Explaining the predictions of any machine learning classifier Probabilistic Programming Primer Original music and sounds by The Sticks.

In this episode of DataFramed, the DataCamp podcast, Hugo speaks with Angela Bassa about managing data science teams. Angela is Director of Data Science at iRobot, where she leads the team through development of machine learning algorithms, sentiment analysis, and anomaly detection processes. iRobot are the makers of consumer robots that we all know and love, like the Roomba, and the Braava which are, respectively, a robotic vacuum cleaner and a robotic mop. Angela will talk about how to get into data science management, the most important strategies to ensure that your data science team delivers value to the organization, how to hire data scientists and key points to consider as your data science team grows over time, in addition to the types of trade-offs you need to make as a data science manager and how you make the right ones. Along the way, you’ll see why a former marine biologist has the skills and ways of thinking to be a super data scientist at a company like iRobot and you’ll also see the importance of throwing data analysis parties.LINKS FROM THE SHOW FROM THE INTERVIEW Angela on TwitterHBR NewslettersiRobot CareersData Science Internship FROM THE SEGMENTS Correcting Data Science Misconceptions (w/ Heather Nolis ~18:45) Using docker to deploy an R plumber API (By Jonathon Nolis)Enterprise Web Services with Neural Networks Using R and TensorFlow (By Jonathan Nolis and Heather Nolis) Project of the Month (w/ David Venturi ~38:45) Rise and Fall of Programming Languages (R Project by David Robinson)Learn, Practice, Apply! (By Ramnath Vaidyanathan)Apply to create a DataCamp project!  Original music and sounds by The Sticks.

Hugo speaks with Peter Bull about the importance of human-centered design in data science. Peter is a data scientist for social good and co-founder of Driven Data, a company that brings cutting-edge practices in data science and crowdsourcing to some of the world's biggest social challenges and the organizations taking them on, including machine learning competitions for social good. They’ll speak about the practice of considering how humans interact with data and data products and how important it is to consider them while designing your data projects. They’ll see how human-centered design provides a robust and reproducible framework for involving the end-user all through the data work, illuminated by examples such as DrivenData’s work in financial services and Mobile Money in Tanzania. Along the way, they’ll discuss the role of empathy in data science, the increasingly important conversation around data ethics and much, much more.LINKS FROM THE SHOW FROM THE INTERVIEW Peter on TwitterDrivenDataDeon (Ethics Checklist)Cookiecutter Data ScienceIf you liked this interview, you might be interested in working with DrivenData! Currently, the team is looking for a software engineer who loves the idea of building Python applications for social impact. Apply Here! FROM THE SEGMENTS Probability Distributions and their Stories (with Justin Bois at ~24:00) Justin's Website at CaltechProbability distributions and their stories (By Justin Bois) Studies in Interpretability (with Peadar Coyle at ~38:10) Interpretable ML SymposiumHow will the GDPR impact machine learning? (By Andrew Burt)How to use Bayesian Stats in your daily job (Gates, Perry, Zorn (2002))Fairness in Machine Learning (By Moritz Hardt) Original music and sounds by The Sticks.

In this episode of DataFramed, a DataCamp podcast, Hugo speaks with Arnaub Chatterjee. Arnaub is a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company. They’ll discuss cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare by looking at practical applications and how McKinsey & Company is helping the industry evolve. Tune in for an insider’s account into what has worked in healthcare, from ML models being used to predict nearly everything in clinical settings, to imaging analytics for disease diagnosis, to wound therapeutics. Will robots and AI replace disciplines such as radiology, ophthalmology, and dermatology? How have the moving parts of data science work evolved in healthcare? What does the future of data science, ML and AI in healthcare hold? Stick around to find out. LINKS FROM THE SHOW FROM THE INTERVIEW McKinsey Analytics on TwitterHot off the press article for HBR’s Future of Healthcare online forum (By Arnaub Chatterjee)Our latest piece on the promise & challenge of AI (By James Manyika and Jacques Bughin)Are robots coming for our jobs? (mckinsey.com)Analytics Careers page (mckinsey.com)How we help clients in healthcare analytics (mckinsey.com)AI analysis of 400+ use cases, including ones in healthcare (By Michael Chui et al. mckinsey.com) FROM THE SEGMENTS Machines that Multi-task (with Manny Moss) Part 1 at ~21:05 Responsible AI in Consumer EnterpriseHilary Mason, DJ Patil and Mike Loukides on Data EthicsEthicalOS Tookit Part 2 at ~40:00 21 Definitions of Fairness Tutorial from FAT* (Arvind Naranayan)Kate Crawford's keynote address "The Trouble with Bias" from NIPS 2017The (im)possibility of Fairness (Sorelle et al. arXiv.org)Learning from disparate data sources (Li Y et al. PubMed.gov)Distributed Multi-task Learning (Liyang Xie et al. KDD.org)The Cost of Fairness in Binary Classification (Aditya Krishna Menon et al. proceedings.mlr.press) Original music and sounds by The Sticks.

In this episode of DataFramed, Hugo speaks with Cassie Kozyrkov, Chief Decision Scientist at Google Cloud. Cassie and Hugo will be talking about data science, decision making and decision intelligence, which Cassie thinks of as data science plus plus, augmented with the social and managerial sciences. They’ll talk about the different and evolving models for how the fruits of data science work can be used to inform robust decision making, along with pros and cons of all the models for embedding data scientists in organizations relative to the decision function. They’ll tackle head on why so many organizations fail at using data to robustly inform decision making, along with best practices for working with data, such as not verifying your results on the data that inspired your models. As Cassie says, “Split your damn data”.Links from the show FROM THE INTERVIEW Cassie on Twitter Is data science a bubble? (By Cassie Kozyrkov, Hackernoon)Incompetence, delegation, and population (By Cassie Kozyrkov, Hackernoon)Populations — You’re doing it wrong (By Cassie Kozyrkov, Hackernoon)What on earth is data science? (By Cassie Kozyrkov, Hackernoon) FROM THE SEGMENTS Probability Distributions and their Stories (with Justin Bois at ~19:45) Justin's Website at CaltechProbability distributions and their stories (By Justin Bois) Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs ~43:45) Sebastian’s Ruder’s Overview of Multi-Task Learning in Deep Neural NetworksMulti-Task Learning for NLP, also by Sebastian RuderGANs for Fake Celebrity Images (Karras et al, Nvidia)Adversarial Multi-Task Learning for Text Classification (Liu et al., arXiv.org) Original music and sounds by The Sticks.

In this episode of DataFramed, Hugo speaks with Brian Granger, co-founder and co-lead of Project Jupyter, physicist and co-creator of the Altair package for statistical visualization in Python. They’ll speak about data science, interactive computing, open source software and Project Jupyter. With over 2.5 million public Jupyter notebooks on github alone, Project Jupyter is a force to be reckoned with. What is interactive computing and why is it important for data science work? What are all the the moving parts of the Jupyter ecosystem, from notebooks to JupyterLab to JupyterHub and binder and why are they so relevant as more and more institutions adopt open source software for interactive computing and data science? From Netflix running around 100,000 Jupyter notebook batch jobs a day to LIGO’s Nobel prize winning discovery of gravitational waves publishing all their results reproducibly using Notebooks, Project Jupyter is everywhere.  Links from the show  FROM THE INTERVIEW Brian on Twitter Project JupyterBeyond Interactive: Notebook Innovation at Netflix (Ufford, Pacer, Seal, Kelley, Netflix Tech Blog)Gravitational Wave Open Science Center (Tutorials)JupyterCon YouTube Playlistjupyterstream Github Repository FROM THE SEGMENTS Machines that Multi-Task (with Friederike Schüür of Fast Forward Labs)Part 1 at ~24:40 Brief Introduction to Multi-Task Learning (By Friederike Schüür)Overview of Multi-Task Learning Use Cases (By Manny Moss)Multi-Task Learning for the Segmentation of Building Footprints (Bischke et al., arXiv.org)Multi-Task as Question Answering (McCann et al., arXiv.org)The Salesforce Natural Language Decathlon: A Multitask Challenge for NLP  Part 2 at ~44:00 Rich Caruana’s Awesome Overview of Multi-Task Learning and Why It WorksSebastian’s Ruder’s Overview of Multi-Task Learning in Deep Neural NetworksMassively Multi-Task Network for Drug Discovery, 259 Tasks (!) (Ramsundar et al. arXiv.org)Brief Overview of Multi-Task Learning with Video of Newsie, the Prototype (By Friederike Schüür)  Original music and sounds by The Sticks.

Hugo speaks with Andrew Gelman about statistics, data science, polling, and election forecasting. Andy is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University and this week we’ll be talking the ins and outs of general polling and election forecasting, the biggest challenges in gauging public opinion, the ever-present challenge of getting representative samples in order to model the world and the types of corrections statisticians can and do perform. "Chatting with Andy was an absolute delight and I cannot wait to share it with you!"-Hugo   Links from the show  FROM THE INTERVIEW Andrew's Blog Andrew on Twitter We Need to Move Beyond Election-Focused Polling (Gelman and Rothschild, Slate)We Gave Four Good Pollsters the Same Raw Data. They Had Four Different Results (Cohn, The New York Times).19 things we learned from the 2016 election (Gelman and Azari, Science, 2017)The best books on How Americans Vote (Gelman, Five Books)The best books on Statistics (Gelman, Five Books)Andrew's Research  FROM THE SEGMENTS Statistical Lesson of the Week (with Emily Robinson at ~13:30) The five Cs (Loukides, Mason, and Patil, O'Reilly) Data Science Best Practices (with Ben Skrainka~40:40) Oberkampf & Roy’s Verification and Validation in Scientific Computing provides a thorough yet very readable treatment A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing (Roy and Oberkampf, Science Direct)  Original music and sounds by The Sticks.

Hugo speaks with Vicki Boykis about what full-stack end-to-end data science actually is, how it works in a consulting setting across various industries and why it’s so important in developing modern data-driven solutions to business problems. Vicki is a full-stack data scientist and senior manager at CapTech Consulting, working on projects in machine learning and data engineering. They'll also discuss the increasing adoption of data science in the cloud technologies and associated pitfalls, along with how to equip businesses with the skills to maintain the data products you developed for them. All this and more: Hugo is pumped! Links from the show FROM THE INTERVIEW Vicki's Tech Blog Vicki on Twitter CapTech Consulting Vicki's Tweet about Programming Building a Twitter art bot with Python, AWS, and socialist realism art FROM THE SEGMENTS Data Science Best Practices (with Ben Skrainka~15:00) Cross-industry standard process for data mining Fundamentals of Machine Learning for Predictive Data Analytics Statistical Lesson of the Week (with Emily Robinson at ~32:05) Sex Bias in Graduate Admissions: Data from Berkeley (Bickel et al., Science, 1975) Time Series Analysis Tutorial with Python Original music and sounds by The Sticks.

Hugo speaks with Allen Downey about uncertainty in data science. Allen is a professor of Computer Science at Olin College and the author of a series of free, open-source textbooks related to software and data science. Allen and Hugo speak about uncertainty in data science and how we, as humans, are not always good at thinking about uncertainty, which we need be to in such an uncertain world. Should we have been surprised at the outcome of the 2016 election? What approaches can we, as a data reporting community, take to communicate around uncertainty better in the future? From election forecasting to health and safety, thinking about uncertainty and using data & data-oriented tools to communicate around uncertainty are essential. Links from the show FROM THE INTERVIEW Data Science Data Optimism Allen's Twitter List of cognitive biases Why are we so surprised? (Allen's Blog) Probably Overthinking It (Allen Downey's Blog) Think Stats (Allen's Book) There is only one test! (Allen's Blog) FROM THE SEGMENT Statistical Distributions and their Stories (with Justin Bois at ~27:00) Justin's Website at Caltech Probability distributions and their stories LeBron James Field Goals Original music and sounds by The Sticks.

Hugo speaks with Renee Teate about the many paths to becoming a data scientist. Renee is a Data Scientist at higher ed analytics start-up HelioCampus, and creator and host of the Becoming a Data Scientist Podcast. In addition to discussing the many possible ways to become becoming a data scientist, they will discuss the common data scientist profiles and how to figure out which ones may be a fit for you. They’ll also dive into the fact that you need to figure out both where you are in terms of skills and knowledge and where you want to go in terms of your career. Renee has a bunch of great suggestions for aspiring data scientists and also flags several important pitfalls and warnings. On top of this, they'll dive into how much statistics, linear algebra and calculus you need to know in order to become an effective data scientist and/or data analyst. Links from the show FROM THE INTERVIEW Becoming a Data Scientist (Renée's Blog) Renée's Twitter Data Sci Guide (Data Science Learning Directory) FROM THE SEGMENTS Statistical Distributions and their Stories (with Justin Bois at ~19:20) Justin's Website at Caltech Probability distributions and their stories Programming Topic of the Week (with Emily Robinson at ~43:20) Categorical Data in the Tidyverse, a DataCamp Course taught by Emily Robinson. R for Data Science Book by Hadley Wickham (Factors Chapter) Inference for Categorical Data, a DataCamp Course taught by Andrew Bray. stringsAsFactors: An unauthorized biography (Roger Peng, July 24, 2015) Wrangling categorical data in R (Amelia McNamara & Nicholas J Horton, August 30, 2017) Original music and sounds by The Sticks.

Hugo speaks with Eric Colson, Chief Algorithms Officer at Stitch Fix, an online personal styling service reinventing the shopping experience by delivering one-to-one personalization to their clients through the combination of data science and human judgment. Eric is responsible for the creation of dozens of algorithms at Stitch Fix that are pervasive to nearly every function of the company, from merchandise, inventory, and marketing to forecasting and demand, operations, and the styling recommender system. Join for all of this and more. Links from the show FROM THE INTERVIEW Stitch Fix Algorithm Tour Warehouse Maps, Movie Recommendation, Structural Biology Advice for Data Scientists on where to work More Human Humans: how our work-life can be improved by ceding tasks to machines. Learning from Textual Feedback (natural Language processing) Deep Style: Teaching machines about style from images Hybrid Designs You Can’t Make this stuff up … or can you? The Blissful Ignorance of the Narrative Fallacy FROM THE SEGMENTS Blog Post of the Week (with Emily Robinson) Doing Good Data Science by Mike Loukides, Hilary Mason and DJ Patil Original music and sounds by The Sticks.

Meet Tanya Cashorali, a founding partner of TCB Analytics, a Boston-based data consultancy. Tanya started her career in bioinformatics and has applied her experience to other industries such as healthcare, finance, retail, and sports. We’ll be talking about what it means to be a data consultant, the wide range of industries that Tanya works in, the impact of data products in her work and the importance of rapid prototyping and getting MVPs or minimum viable products out the door. How does Tanya balance the trade-off between rapid prototyping and building fully mature data products? How does this play out in particular cases in the healthcare and telecommunications spaces? How has her ability to do this evolved as a function of open source software development? We’ll also dive into how general data literacy has evolved, how it can help decision making in business more generally, the data science skills gap and how many data science hiring processes are broken and how to fix them.

Hugo speaks with JD Long, VP of risk management for Renaissance reinsurance, about applications of data science techniques to the omnipresent worlds of insurance, reinsurance, risk management and uncertainty. What are the biggest challenges in insurance and reinsurance that data science can impact? How does JD go about building risk representations of every deal? How can thinking in a distributed fashion allow us to think about risk and uncertainty? What is the role of empathy in data science?

Hugo speaks with Christie Bahlai, Assistant Professor at Kent State University, about data science, ecology, and the adoption of techniques such as machine learning in academic research. What are the biggest challenges in ecology that data science can help to solve? What does the intersection of open science and data science look like? In scientific research, what is happening at the interface between data science & machine learning methods, which are pattern-based, and traditional research methods, which are classically hypothesis driven? Is there a paradigm shift occurring here? Listen to find out! Links from the show The Bahlai Lab of applied quantitative ecology Christie Bahlai on twitter Hugo's article on What Data Scientists Really Do in Harvard Business Review Hugo's webinar on What Managers Need To Know About Machine Learning

Hugo speaks with Yves Hilpisch about how data science is disrupting finance. Yves’ name is synonymous with Python for Finance and he is founder and managing partner of The Python Quants, a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading and computational finance. Why are banks such as Bank of America & JP Morgan adopting the open source data science ecosystem? What are the major sub-disciplines of Finance that data science is and can have a large impact in? How has the rise of data science changed the financial world and how the work is done and thought about? Stick around to find out.

Hugo speaks with Amber Thomas about data journalism, interactive visualization and data storytelling. Amber is a journalist-engineer at The Pudding, which is a collection of data-driven, visual essays. We’ll discuss the ins and outs of what it takes to tell interactive journalistic stories using data visualization and, in the process, we’ll find out what it takes to be successful at data journalism, the trade-off between being being a generalist and specialist and much more. We’ll explore these issues by focusing on several case studies, including a piece that Amber worked on late last year called “How far is too far? An analysis of driving times to abortion clinics in the US.”

What are the biggest challenges in Pharmaceuticals that data science can help to solve? How are data science and statistics generally embedded in organizations such as Pfizer? What aspects of the pharmaceutical business run the gamut of nonclinical statistics? Hugo speaks with Max Kuhn, a software engineer at RStudio who was previously Senior Director of Nonclinical Statistics at Pfizer Global R&D. Max was applying models in the pharmaceutical and diagnostic industries for over 18 years.

Hugo speaks with Derek Johnson, an epidemiologist with Doctors without Borders. Derek leverages statistical methods, experimental design and data scientific techniques to investigate the barriers impeding people from accessing health care in Lahe Township, Myanmar. If you thought data science was all machine learning, SQL databases and convolutional neural nets, this is gonna be a wild ride as to get the data for their baseline health assessments, Derek and his team ride motorcycles into villages in northern Myanmar for weeks on end to perform in person surveys, equipped with translators and pens and paper because they can’t be guaranteed of electricity. Derek also researches the factors associated with the transmission of hepatitis C between family members and has helped to conduct studies in Uganda, Nepal, and India. All this and more.

Hugo speaks with Alan Nichol about chatbots, conversational software and data science. Alan is co-founder and CTO of Rasa, who build open source machine learning tools for developers and product teams to expand bots beyond answering simple questions. Which verticals are conversational software currently having the biggest impact on? What are the biggest challenges facing the fields of chatbots and conversational software? What misapprehensions do we as a society have about these technologies that experts such as Alan would like to correct? And how can we all build chatbots and conversational software ourselves?

Hugo speaks with Taras Gorishnyy, a Senior Analytics Manager at McKinsey and Head of Data Science at QuantumBlack, a McKinsey company. They discuss the role of data science in management consulting, what it takes to change organizations through data science, how the different moving parts of data science have evolved over the past decade and in which direction they’re heading. You’ll see the impact that data science can have not only in tech, but also in such various verticals as retail, agriculture and the penal system. Taras will also take us through the 5 steps required to change organizations through data science, all of which are necessary. Can you guess what they are? We're really excited to have Taras on the show as DataCamp has had a long relationship with McKinsey, including that McKinsey uses DataCamp for training.

Omoju Miller, a Senior Machine Learning Data Scientist with Github, speaks with Hugo about the role of data science in product development at github, what it means to “use computation to build products to solve real-life decision making, practical challenges” and what building data products at github actually looks like. Machine learning has the power to automate so much of the drudgery around data science & software engineering, from automated code review to flagging security vulnerabilities in code, and from recommending repositories to contributors to matching issues with maintainers and contributors and identifying duplicate issues. And just in case that’s not enough, they'll discuss github as a platform for work, not just technical, and, as Omoju has called it, “a collaborative work environment centered around humans.”

What are best practices for organizing data science teams? Having data scientists distributed through companies or having a Centre of Excellence? What are the most important skills for data scientists? Is the ability to use the most sophisticated deep learning models more important than being able to make good powerpoint slides? Find out in this conversation with Jacqueline Nolis, a data science leader in the Seattle area with over a decade of experience. Jacqueline is currently running a consulting firm helping Fortune 500 companies with data science, machine learning, and AI. This interview is with Jacqueline Nolis, but at the time of recording, she went by Jonathan Nolis.  Links from the show  Jacqueline Nolis' website You're relying on data too much: making decisions worse, not better, by Jacqueline Nolis Hiring data scientists (part 1): what to look for in a candidate, by Jacqueline Nolis Jacqueline on Twitter For more, see our page here 

What are the biggest challenges currently facing data security and privacy? What does the GDPR mean for civilians, working data scientists and businesses around the world? Is data anonymization actually possible or a pipe dream? Find out in Hugo's conversation with Katharine Jarmul, a data scientist, consultant, educator and co-founder of KI protect, a company that provides real-time protection for your data infrastructure, data science and AI. Links from the show KI Protect, providing real-time protection for your data infrastructure. What is GDPR? The summary guide to GDPR compliance in the UK by Matt Burgess for Wired Apple's differential privacy approach For more, see our page here

Why are spreadsheets ubiquitous in data analytics, why are so many data scientists anti-spreadsheet? Join Jenny Bryan, a software engineer at RStudio & recovering biostatistician who takes special delight in eliminating the small agonies of data analysis, and Hugo to discover why spreadsheets are in fact necessary in data analytics and how spreadsheet workflows can be incorporated into more general data science flows in sustainable and healthy ways. Welcome to the future. Links from the show Best Practices for Using Google Sheets in Your Data Project Jenny Bryan's repository of scary Excel stories Sanesheets, a self-proclaimed > by Jenny Bryan DataCamp's first two free courses on spreadsheets

Community building is an essential aspect of data science. But how do you do it? Find out in Hugo's conversation with Jared Lander, organizer of the New York Open Statistical Programming Meetup and the New York R Conference. Jared is also the Chief Data Scientist of Lander Analytics, a data science consultancy based in New York City and an Adjunct Professor of Statistics at Columbia University. How does Jared think about creating safe and welcoming spaces for budding and practicing data scientists of all ilk? How does he put this into practice? How does he make people feel comfortable and at home in a field in which so many intelligent and curious people feel like imposters? What practical & specific considerations are there in creating this home for underrepresented groups? How does he stay ahead of the curve in terms of modern, up-to-date content and speakers for his meetup and conference?

"Cloud computing is a huge revolution in the computing space, and it's also probably going to be one of the most transformative technologies that any of us experience in our lifetime. " Paige Bailey, Senior Cloud Developer Advocate at Microsoft, in this episode of DataFramed. In this conversation with Hugo, Paige reports from the frontier of cloud-based data science technologies, having just been at the Microsoft Build and Google I/O conferences. What is the future of data science in the cloud? How can you get started? Stick around to find out and much, much more.

What do online experiments, data science and product development look like at Booking.com, the world’s largest accommodations provider? Join Hugo's conversation with Lukas Vermeer to find out. Lukas is responsible for experimentation at Booking in the broadest sense of the word: from Infrastructure and Tools used to run experiments, Methodology and Metrics that help people make decisions to Training and Culture that help people understand what to do. They'll be talking about how Booking leverages Data Science to help empower people to experience the world through the three pillars of exploratory analysis, qualitative research and quantitative studies. They'll also take a deep dive into the fact that data science isn't actually anywhere near as objective as you may think.

Building models of the world is dangerous and there are pitfalls everywhere, even down to the assumptions that you make. To find out about many statistical pitfalls, and how to build more robust data scientific models using statistical modeling, whether it be in tech, epidemiology, finance or anything else, join Hugo's chat with Michael Betancourt, a physicist, statistician and one of the core developers of the open source statistical modeling platform Stan.

How can data science help in the fight against cancer? What are its limitations? Find out in this conversation from the frontier of research. Hugo speaks with Sandy Griffith from Flatiron Health, a healthcare technology and services company focused on accelerating cancer research and improving patient care. Sandy is Principal methodologist on Flatiron's Quantitative Sciences team and is tasked with leveraging data science "To improve lives by learning from the experience of every cancer patient".

Anthony Goldbloom, CEO of Kaggle, speaks with Hugo about Kaggle, data science communities, reproducible data science, machine learning competitions and the future of data science in the cloud. If you thought that Kaggle was merely a platform for machine learning competitions, you have to check out this chat, because these ML comps account for less than a third of activity on Kaggle today. In the discussion: Kaggle kernels for reproducible data science and the evolution of the Kaggle public data platform; the genesis of Kaggle and how Anthony managed to solve the cold start problem of building a two-sided market place; the exciting implications of Kaggle's recent acquisition by Google for the future of cloud-based data science; why Python is dominant on Kaggle.

"We should be looking at Automated Machine Learning tools as more like data science assistants, rather than replacements for data scientists" -- Randy Olson, Lead Data Scientist at Life Epigenetics, Inc. Randy specializes in artificial intelligence, machine learning, and created TPOT, a Data Science Assistant and a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Will the future of data science be automated? Which verticals will experience the largest disruption? What will the role of data science become? There's one way to find out: jump straight into this chat with Randy and Hugo.

Michelle Gill, a deep learning expert at NVIDIA, an Artificial Intelligence company that builds GPUs, the processors that everybody uses for deep learning, speaks with Hugo about the modern superpower of deep learning and where it has the largest impact, past, present and future, filtered through the lens of Michelle's work at NVIDIA. Where is the modern superpower of deep learning most effective? Where is it not? Where should we channel our skepticism of the hype surrounding it?

Sebastian Raschka, a machine learning aficionado, data analyst, author, python programmer, open source contributor, computational biologist, and occasional blogger, speaks with Hugo about the role of data science in modern biology and the power of deep learning in today's rapidly evolving data science landscape. How is Sebastian using deep learning to build facial recognition software that also prevents racial and gender profiling? Check out this week's episode to find out.

Drew Conway, world-renowned data scientist, entrepreneur, author, speaker and creator of the Data Science Venn Diagram speaks with Hugo about how to build data science teams, along with the unique challenges of building data science products for industrial users. How does Drew now view the Venn circles he created, those of hacking skills, mathematical and statistical knowledge and substantive expertise, when building out data science teams?

Fake news: how can data science and deep learning be leveraged to detect it? Come on a journey with Mike Tamir, Head of Data Science at Uber ATG, who is building out a data science product that classifies text as news, editorial, satire, hate speech and fake news, among others. We'll also see what types of unique challenges Mike faced in his work at Takt, using data science to service the needs of Fortune 500 companies such as Starbucks.Links from the show FROM THE INTERVIEW FakerFact(Chrome Extension)FakerFact (Firefox Extension)FakerFact The Unreasonable Effectiveness of Recurrent Neural Networks by Andrei Karpathy FROM THE SEGMENTS The Double-edged Sword of Impact Parts I & 2 (with Friederike Schüür, Cloudera Fast Forward Labs) Media Manipulation and Disinformation Online from Data & SocietyJames Bridle's blog post 'Something is wrong on the internet'The Cost of Fairness in Binary Classification (.pdf), a paper by Menon & Williamson (2018)Multisided Fairness for Recommendation, a paper by Burke (2017)All The Cool Kids, How Do They Fit In? Popularity and Demographic Biases in Recommender Evaluation and Effectiveness, a paper by Ekstrand et al. (2018)The spread of true and false news online, a paper by Vosoughi et al. (2018) Original music and sounds by The Sticks.

Nuclear engineering, data science and open source software development: where do these all intersect? To find out, join Hugo and Katy Huff, Assistant Professor in the Department of Nuclear, Plasma, and Radiological Engineering at the University of Illinois where she leads the Advanced Reactors and Fuel Cycles research group.

How does data science help Buzzfeed achieve online virality? What type of mass online experiments do data scientists at BuzzFeed run for this purpose? What products do they develop to make all of this easy and intuitive for content producers? Find out about all of this and more in this episode when Hugo talks with Adam Kelleher, Principal Data Scientist at BuzzFeed and Adjunct Assistant Professor at Columbia University. They'll also dive into the role of thinking about causality in modern data science.

Air pollution, the environment and data science: where do these intersect? Find out in this episode of DataFramed, in which Hugo speaks with Roger Peng, Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, co-director of the Johns Hopkins Data Science Lab and co-founder of the Johns Hopkins Data Science Specialization. Join our discussion about data science, it's role in researching the environment and air pollution, massive open online courses for democratizing data science and much more.

Etsy, online experiments and data science are the topics of this episode, in which Hugo speaks with Emily Robinson, a data analyst at Etsy. How are data science and analysis integral to their business and decision making? Join us to find out. We'll also dive into the types of statistical modeling that occurs at Etsy and the importance of both diversity and community in data science.

Jake VanderPlas, a data science fellow at the University of Washington's eScience Institute, astronomer, open source beast and renowned Pythonista, joins Hugo to speak about data science, astronomy, the open source development world and the importance of interdisciplinary conversations to data science.

Airbnb's business depends on data science. In this episode, Hugo speaks with Robert Chang, data scientist at airbnb and previously at twitter. We'll be chatting about the different types of roles data science can play in digital businesses such as airbnb and twitter, how companies at different stages of development actually require divergent types of data science to be done, along with the different models for how data scientists are placed within companies, from the centralized model to the embedded to the hybrid: can you guess which is Robert's favourite? This is a hands-on, practical look at how data science works at airbnb and digital businesses in general.

Maelle Salmon, a data scientist who has worked in public health, both in infectious disease and environmental epidemiology, joins Hugo for a chat about the role of data science, statistics and data management in researching the health effects of air pollution and urbanization. In the process, we'll dive into the continual need for open source toolbox development, open data, knowledge organisation and diversity in this emerging discipline.

The trucking industry is being revolutionized by Data Science. And how? Hugo speaks with Ben Skrainka, a data scientist at Convoy, a company that provides trucking services for shippers and carriers powered by technology to drive reliability, transparency, efficiency, and insights. We'll dive into how data science can help to achieve such a trucking revolution, and how this will impact all of us, from truckers to businesses and consumers alike. Along the way, we'll delve into Ben's thoughts on best practices in data science, how the field is evolving and how we can all help to shape the future of this emerging discipline.

Chris Volinsky, AT&T Labs' Assistant Vice President for Big Data Research and a member of the team that won the $1M Netflix Prize, an open competition for improving Netflix' online recommendation system, speaks with Hugo. We'll be discussing the role data science plays in the modern telecommunications network landscape, how it helps a company that services over 140 million customers and what statistical and data scientific techniques his team uses to work with such large amounts of data. Along the way, we'll dive into the need for more transparency concerning the use of civilian data and Chris's work on the Netflix recommendation system prize.

David Robinson, a data scientist at Stack Overflow, joins Hugo to speak about the evolving importance of citizen data science and a future in which data literacy is considered a necessary skill to navigate the world, similar to literacy today. We'll speak about many of Dave projects, including his analysis of Trump's tweets that demonstrated the stark contrast between Trump's own tweets and those of his PR machine. We'll also speak about ways for journalists, software engineers, scientists and all walks of life to get up and running doing data science and analysis.

Claudia Perlich, Chief Scientist at DStillery, a role in which she designs, develops, analyzes and optimizes the machine learning algorithms that drive digital advertising, speaks with Hugo about the role of data science in the online advertising world, the predictability of humans, how her team builds real time bidding algorithms and detects bots online, along with the ethical implications of all of these evolving concepts.

Hilary Mason talks about the past, present, and future of data science with Hugo. Hilary is the VP of Research at Cloudera Fast Forward, a machine intelligence research company, and the data scientist in residence at Accel. If you want to hear about where data science has come from, where it is now, and the direction it's heading, you've come to the right place. Along the way, we'll delve into the ethics of machine learning, the challenges of AI, automation and the roles of humanity and empathy in data science.

We are super pumped to be launching a weekly data science podcast called DataFramed, in which Hugo Bowne-Anderson, a data scientist and educator at DataCamp, speaks with industry experts about what data science is, what it’s capable of, what it looks like in practice and the direction it is heading over the next decade and into the future. Check out this snippet for a sneak preview!