Data science leadership is about more than just technical expertise—it’s about building trust, embracing AI, and delivering real business impact. As organizations evolve toward AI-first strategies, data teams have an unprecedented opportunity to lead that transformation. But how do you turn a traditional analytics function into an AI-driven powerhouse that drives decision-making across the business? What’s the right structure to balance deep technical specialization with seamless business integration? From building credibility through high-impact forecasting to creating psychological safety around AI adoption, effective data leadership today requires both technical rigor and visionary communication. The landscape is shifting fast, but with the right approach, data science can stand as a true pillar of innovation alongside engineering, product, and design. Bilal Zia is currently the Head of Data Science & Analytics at Duolingo, an EdTech company whose mission is to develop the best education in the world and make it universally available. Previously, he spent two years helping to build and lead an interdisciplinary Central Science team at Amazon, comprising economists, data and applied scientists, survey specialists, user researchers, and engineers. Before that, he spent fifteen years in the Research Department of the World Bank in Washington, D.C., pursuing an applied academic career. He holds a Ph.D. in Economics from the Massachusetts Institute of Technology, and his interests span economics, data science, machine learning/AI, psychology, and user research. In the episode, Richie and Bilal explore rebuilding an underperforming data team, fostering trust with leadership, embedding data scientists within product teams, leveraging AI for productivity, the future of synthetic A/B testing, and much more. Links Mentioned in the Show: DuolingoDuolingo Blog: How machine learning supercharged our revenue by millions of dollarsConnect with BilalAI-Native Course: Intro to AI for WorkRelated Episode: The Future of Data & AI Education Just Arrived with Jonathan Cornelissen & Yusuf SaberRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The future of education is being reshaped by AI-powered personalization. Traditional online learning platforms offer static content that doesn't adapt to individual needs, but new technologies are creating truly interactive experiences that respond to each learner's context, pace, and goals. How can personalized AI tutoring bridge the gap between mass education and the gold standard of one-on-one human tutoring? What if every professional could have a private tutor that understands their industry, role, and specific challenges? As organizations invest in upskilling their workforce, the question becomes: how can we leverage AI to make learning more engaging, effective, and accessible for everyone? As the Co-Founder & CEO of DataCamp, Jonathan Cornelissen has 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 Ph.D. in financial econometrics and was the original author of an R package for quantitative finance. Yusuf Saber is a technology leader and entrepreneur with extensive experience building and scaling data-driven organizations across the Middle East. He is the Founder of Optima and a Venture Partner at COTU Ventures, with previous leadership roles at talabat, including VP of Data and Senior Director of Data Science and Engineering. Earlier in his career, he co-founded BulkWhiz and Trustious, and led data science initiatives at Careem. Yusuf holds research experience from ETH Zurich and began his career as an engineering intern at Mentor Graphics. In the episode, Richie, Jo and Yusuf explore the innovative AI-driven learning platform Optima, its unique approach to personalized education, the potential for AI to enhance learning experiences, the future of AI in education, the challenges and opportunities in creating dynamic, context-aware learning environments, and much more. Links Mentioned in the Show: Read more about the announcementTry the AI-Native Courses:Intro to SQLIntro to AI for Work New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for busines

The line between human work and AI capabilities is blurring in today's business environment. AI agents are now handling autonomous tasks across customer support, data management, and sales prospecting with increasing sophistication. But how do you effectively integrate these agents into your existing workflows? What's the right approach to training and evaluating AI team members? With data quality being the foundation of successful AI implementation, how can you ensure your systems have the unified context they need while maintaining proper governance and privacy controls? Karen Ng is the Head of Product at HubSpot, where she leads product strategy, design, and partnerships with the mission of helping millions of organizations grow better. Since joining in 2022, she has driven innovation across Smart CRM, Operations Hub, Breeze Intelligence, and the developer ecosystem, with a focus on unifying structured and unstructured data to make AI truly useful for businesses. Known for leading with clarity and “AI speed,” she pushes HubSpot to stay ahead of disruption and empower customers to thrive. Previously, Karen held senior product leadership roles at Common Room, Google, and Microsoft. At Common Room, she built the product and data science teams from the ground up, while at Google she directed Android’s product frameworks like Jetpack and Jetpack Compose. During more than a decade at Microsoft, she helped shape the company’s .NET strategy and launched the Roslyn compiler platform. Recognized as a Product 50 Winner and recipient of the PM Award for Technical Strategist, she also advises and invests in high-growth technology companies. In the episode, Richie and Karen explore the evolving role of AI agents in sales, marketing, and support, the distinction between chatbots, co-pilots, and autonomous agents, the importance of data quality and context, the concept of hybrid teams, the future of AI-driven business processes, and much more. Links Mentioned in the Show: Hubspot Breeze AgentsConnect with KarenWebinar: Pricing & Monetizing Your AI Products with Sam Lee, VP of Pricing Strategy & Product Operations at HubSpotRelated Episode: Enterprise AI Agents with Jun Qian, VP of Generative AI Services at OracleRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The relationship between AI and data professionals is evolving rapidly, creating both opportunities and challenges. As companies embrace AI-first strategies and experiment with AI agents, the skills needed to thrive in data roles are fundamentally changing. Is coding knowledge still essential when AI can generate code for you? How important is domain expertise when automated tools can handle technical tasks? With data engineering and analytics engineering gaining prominence, the focus is shifting toward ensuring data quality and building reliable pipelines. But where does the human fit in this increasingly automated landscape, and how can you position yourself to thrive amid these transformations? Megan Bowers is Senior Content Manager, Digital Customer Success at Alteryx, where she develops resources for the Maveryx Community. She writes technical blogs and hosts the Alter Everything podcast, spotlighting best practices from data professionals across the industry. Before joining Alteryx, Megan worked as a data analyst at Stanley Black & Decker, where she led ETL and dashboarding projects and trained teams on Alteryx and Power BI. Her transition into data began after earning a degree in Industrial Engineering and completing a data science bootcamp. Today, she focuses on creating accessible, high-impact content that helps data practitioners grow. Her favorite topics include switching career paths after college, building a professional brand on LinkedIn, writing technical blogs people actually want to read, and best practices in Alteryx, data visualization, and data storytelling. Presented by Alteryx, Alter Everything serves as a podcast dedicated to the culture of data science and analytics, showcasing insights from industry specialists. Covering a range of subjects from the use of machine learning to various analytics career trajectories, and all that lies between, Alter Everything stands as a celebration of the critical role of data literacy in a data-driven world. In the episode, Richie and Megan explore the impact of AI on job functions, the rise of AI agents in business, and the importance of domain knowledge and process analytics in data roles. They also discuss strategies for staying updated in the fast-paced world of AI and data science, and much more. Links Mentioned in the Show: Alter EverythingConnect with MeganSkill Track: Alteryx FundamentalsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Data science continues to evolve in the age of AI, but is it still the 'sexiest job of the 21st century'? While generative AI has transformed the landscape, it hasn't replaced data scientists—instead, it's created more demand for their skills. Data professionals now incorporate AI into their workflows to boost efficiency, analyze data faster, and communicate insights more effectively. But with these technological advances come questions: How should you adapt your skills to stay relevant? What's the right balance between traditional data science techniques and new AI capabilities? And as roles like analytics engineer and machine learning engineer emerge, how do you position yourself for success in this rapidly changing field? Dawn Choo is the Co-Founder of Interview Master, a platform designed to streamline technical interview preparation. With a foundation in data science, financial analysis, and product strategy, she brings a cross-disciplinary lens to building data-driven tools that improve hiring outcomes. Her career spans roles at leading tech firms, including ClassDojo, Patreon, and Instagram, where she delivered insights to support product development and user engagement. Earlier, Dawn held analytical and engineering positions at Amazon and Bank of America, focusing on business intelligence, financial modeling, and risk analysis. She began her career at Facebook as a marketing analyst and continues to be a visible figure in the data science community—offering practical guidance to job seekers navigating technical interviews and career transitions. In the episode, Richie and Dawn explore the evolving role of data scientists in the age of AI, the impact of generative AI on workflows, the importance of foundational skills, and the nuances of the hiring process in data science. They also discuss the integration of AI in products and the future of personalized AI models, and much more. Links Mentioned in the Show: Interview MasterConnect with DawnDawn’s Newsletter: Ask Data DawnGet Certified: AI Engineer for Data Scientists Associate CertificationRelated Episode: How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib AcademyRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Healthcare AI is rapidly evolving beyond simple diagnostic tools to comprehensive systems that can analyze and predict patient outcomes. With the rise of multimodal AI models that can process everything from medical images to patient records and genetic information, we're entering an era where AI could fundamentally transform how healthcare decisions are made. But how do we ensure these systems maintain patient privacy while still leveraging vast amounts of medical data? What are the technical challenges in building AI that can reason across different types of medical information? And how do we balance the promise of AI-assisted healthcare with the critical role of human medical professionals? Professor Aldo Faisal is Chair in AI & Neuroscience at Imperial College London, with joint appointments in Bioengineering and Computing, and also holds the Chair in Digital Health at the University of Bayreuth. He is the Founding Director of the UKRI Centre for Doctoral Training in AI for Healthcare and leads the Brain & Behaviour Lab and Behaviour Analytics Lab at Imperial’s Data Science Institute. His research integrates machine learning, neuroscience, and human behaviour to develop AI technologies for healthcare. He is among the few engineers globally leading their own clinical trials, with work focused on digital biomarkers and AI-based medical interventions. Aldo serves as Associate Editor for Nature Scientific Data and PLOS Computational Biology, and has chaired major conferences like KDD, NIPS, and IEEE BSN. His work has earned multiple awards, including the $50,000 Toyota Mobility Foundation Prize, and is regularly featured in global media outlets. In the episode, Richie and Aldo explore the advancements in AI for healthcare, including AI's role in diagnostics and operational improvements, the ambitious Nightingale AI project, challenges in handling diverse medical data, privacy concerns, and the future of AI-assisted medical decision-making, and much more. Links Mentioned in the Show: Aldo’s PublicationsConnect with AldoProject: What is Your Heart Rate Telling You?Related Episode: Using Data to Optimize Costs in Healthcare with Travis Dalton and Jocelyn Jiang President/CEO & VP of Data & Decision Science at MultiPlanRewatch RADAR AI New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The modern data stack has transformed how organizations work with data, but are our BI tools keeping pace with these changes? As data schemas become increasingly fluid and analysis needs range from quick explorations to production-grade reporting, traditional approaches are being challenged. How can we create analytics experiences that accommodate both casual spreadsheet users and technical data modelers? With semantic layers becoming crucial for AI integration and data governance growing in importance, what skills do today's BI professionals need to master? Finding the balance between flexibility and governance is perhaps the greatest challenge facing data teams today. Colin Zima is the Co-Founder and CEO of Omni, a business intelligence platform focused on making data more accessible and useful for teams of all sizes. Prior to Omni, he was Chief Analytics Officer and VP of Product at Looker, where he helped shape the product and data strategy leading up to its acquisition by Google for $2.6 billion. Colin’s background spans roles in data science, analytics, and product leadership, including positions at Google, HotelTonight, and as founder of the restaurant analytics startup PrimaTable. He holds a degree in Operations Research and Financial Engineering from Princeton University and began his career as a Structured Credit Analyst at UBS. In the episode, Richie and Colin explore the evolution of BI tools, the challenges of integrating casual and rigorous data analysis, the role of semantic layers, and the impact of AI on business intelligence. They discuss the importance of understanding business needs, creating user-focused dashboards, and the future of data products, and much more. Links Mentioned in the Show: OmniConnect with ColinSkill Track: Design in Power BIRelated Episode: Self-Service Business Intelligence with Sameer Al-Sakran, CEO at MetabaseRegister for RADAR AI - June 26 New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

GPU acceleration is transforming how data scientists tackle computationally intensive problems in the AI and materials science fields. When dealing with billions of potential molecular combinations or massive datasets requiring dimensionality reduction, traditional CPU approaches often become prohibitively slow and expensive. How can data professionals determine when GPU acceleration will provide meaningful benefits to their workflows? Understanding the right applications for this technology can mean the difference between waiting hours versus minutes for critical results. Nick Becker is a Group Product Manager at NVIDIA, focused on building RAPIDS and the broader accelerated data science ecosystem. Nick has a professional background in technology and government. Prior to NVIDIA, he worked at Enigma Technologies, a data science startup. Before Enigma, he conducted economics research and forecasting at the Federal Reserve Board of Governors, the central bank of the United States. Dan Hannah is an Associate Director at SES AI Corporation. At SES, Dan leads a research program focused on discovering new battery materials using machine learning, chemical informatics, and physics-driven simulations. Prior to joining SES, Dan spent several years as a data scientist in the cybersecurity industry. Dan holds a Ph.D. in Physical Chemistry from Northwestern University and did a postdoctoral fellowship at Berkeley National Lab, where his focus was the discovery of novel inorganic materials for energy applications. In the episode, Richie, Nick, and Dan explore the quest for new battery technologies, the role of data science and machine learning in material discovery, the integration of NVIDIA's GPU technology, the balance between computational simulations and lab work, and much more. Links Mentioned in the Show: NVIDIA RAPIDSSES AI CorporationConnect with Dan and NickCareer Track: Machine Learning Scientist in PythonRelated Episode: Data Science Trends from 2 Kaggle Grandmasters with Jean-Francois Puget, Distinguished Engineer at NVIDIA & Chris Deotte, Senior Data Scientist at NVIDIARewatch sessions from RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

In the retail industry, data science is not just about crunching numbers—it's about driving business impact through well-designed experiments. A-B testing in a physical store setting presents unique challenges that require careful planning and execution. How do you balance the need for statistical rigor with the practicalities of store operations? What role does data science play in ensuring that test results lead to actionable insights?  Philipp Paraguya is the Chapter Lead for Data Science at Aldi DX. Previously, Philipp studied applied mathematics and computer science and has worked as a BI and advanced analytics consultant in various industries and projects since graduating. Due to his background as a software developer, he has a strong connection to classic software engineering and the sensible use of data science solutions. In the episode, Adel and Philipp explore the intricacies of A-B testing in retail, the challenges of running experiments in brick-and-mortar settings, aligning stakeholders for successful experimentation, the evolving role of data scientists, the impact of genAI on data workflows, and much more. Links Mentioned in the Show: Aldi DXConnect with PhilippCourse: Customer Analytics and A/B Testing in PythonRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYUSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The integration of speech AI into everyday business operations is reshaping how we communicate and process information. With applications ranging from customer service to quality control, understanding the nuances of speech AI is crucial for professionals. How do you tackle the complexities of different languages and accents? What are the best practices for implementing speech AI in your organization? Explore the transformative power of speech AI and learn how to overcome the challenges it presents in your professional landscape. Alon Peleg serves as the Chief Operating Officer (COO) at aiOla, a position he assumed in May 2024. With over two decades of leadership experience at renowned companies like Wix, Cisco, and Intel, he is widely recognized in the tech industry for his expertise, dynamic leadership, and unwavering dedication. At aiOla, Alon plays a key role in driving innovation and strategic growth, contributing to the company’s mission of developing cutting-edge solutions in the tech space. His appointment is regarded as a pivotal step in aiOla’s expansion and continued success. Gill Hetz is the VP of AI at aiOla where he leverages his expertise in data integration and modeling. Gill was previously active in the oil and gas industry since 2009, holding roles in engineering, research, and data science. From 2018 to 2021, Gill held key positions at QRI, including Project Manager and SaaS Product Manager. In the episode, Richie, Alon, and Gill explore the intricacies of speech AI, its components like ASR, NLU, and TTS, real-world applications in industries such as retail and pharmaceuticals, challenges like accents and background noise, and the future of voice interfaces in technology, and much more. Links Mentioned in the Show: aiOlaConnect with Alon and GillCourse: Spoken Language Processing in PythonRelated Episode: Building Multi-Modal AI Applications with Russ d'Sa, CEO & Co-founder of LiveKitSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

As businesses collect more data than ever, the question arises: is bigger always better? Companies are beginning to question whether massive datasets and complex infrastructures are truly delivering results or just adding unnecessary costs. How can you align your data strategy with your actual needs? Could focusing on smaller, more manageable datasets improve efficiency and save resources while still delivering valuable insights? Dr. Madelaine Daianu is the Head of Data & AI at Credit Karma, Inc. Before joining the company in June 2023, she served as Head of Data and Pricing at Belong Home, Inc. Earlier in her career, Daianu has held numerous senior roles in data science and machine learning at The RealReal, Facebook, and Intuit. Daianu earned a Bachelor of Applied Science in Bioengineering and Mathematics from the University of Illinois at Chicago and a Ph.D. in Bioengineering and Biomedical Engineering from the University of California, Los Angeles. In the episode, Richie and Madelaine explore generative AI applications at Credit Karma, the importance of data infrastructure, the role of explainability in fintech, strategies for scaling AI processes, and much more. Links Mentioned in the Show: Credit KarmaConnect with MaddieSkill Track: AI Business FundamentalsRelated Episode: Effective Product Management for AI with Marily Nika, Gen AI Product Lead at Google AssistantSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

With AI agents and GPU acceleration at the forefront, data science is entering a new era of efficiency and innovation. How are AI copilots transforming the way data scientists code and solve problems? Are they a reliable partner or a source of new complexities? On the other hand, the move to GPU-accelerated data science tools is revolutionizing model training and experimentation. What does this mean for the future of data science workflows? Explore these cutting-edge developments and their impact on the industry. Jean-Francois got a PhD in machine learning in the previous millennium. Given the AI winter at the time, he worked for a while on mathematical optimization software as dev manager for CPLEX in a startup. He came back to Machine Learning when IBM acquired the startup. Since then he discovered Kaggle and became one of the best Kagglers in the world. He joined NVIDIA 5 years ago and leads the NVIDIA Kaggle Grandmaster team there. Chris Deotte is a senior data scientist at NVIDIA. Chris has a Ph.D. in computational science and mathematics with a thesis on optimizing parallel processing. Chris is a Kaggle 4x grandmaster. In the episode, Richie, Jean-Francois, and Chris explore the transformative role of AI agents in data science, the impact of GPU acceleration on workflows, the evolution of competitive data science techniques, the importance of model evaluation and communication skills, and the future of data science roles in an AI-driven world, and much more. Links Mentioned in the Show: NVIDIANVIDIA RapidsFew shot learningConnect with Jean-Francois on Linkedin and Kaggle and check out Chris on KaggleCourse: Winning a Kaggle Competition in PythonRelated Episode: Becoming a Kaggle GrandmasterSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

2024 was another huge year for data and AI. Generative AI continued to shape the way we work and interact with technology, with companies of all sizes racing to integrate AI into their products. We saw strides in tools like AI-enhanced data science notebooks, rapid adoption of generative image AI, and a steady march toward video generation AI. At the same time, foundational skills like AI literacy and data governance gained traction as critical areas for individuals and organizations to master. This time last year, DataCamp Co-Founders Jonathan and Martijn made a series of predictions and data and AI for 2024, today, they join Richie to reflect on their 2024 predictions and share their vision for data and AI in 2025. In the episode, Richie, Jonathan, and Martijn review the mainstream adoption of generative AI and its journey toward daily use, the rise of AI literacy as a critical skill, the growing overlap between data science and software engineering with the emergence of AI engineers, evolving trends in programming languages, how generative AI has moved from prototype to production, the near-mainstreaming of video generation AI, why AI hype continues to thrive and much more. Links Mentioned in the Show: Data & AI Trends & Predictions 2025Skill Track: AI Business FundamentalsRelated Episode: Data Trends & Predictions 2024 with DataCamp's CEO & COO, Jonathan Cornelissen & Martijn TheuwissenRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. Imagine spending millions on data tools only to find you can’t trust the answers they provide. What if different teams define key metrics in different ways? Without a clear, unified approach, chaos reigns, and confidence erodes. What role do data governance and semantic layers play in helping you trust the AI tools you build and the insights you get from your data? Sarah Levy is a seasoned executive with extensive experience in data science, artificial intelligence, and technology leadership. Currently serving as Co-Founder and CEO of Euno since January 2023, Sarah has previously held significant positions, including VP of Data Science and Data Analytics for Real Estate at Pagaya and CTO at Sight Diagnostics, where innovative advancements in blood testing were achieved. With a strong foundation in research and development from roles at Sight Diagnostics and Natural Intelligence, as well as a robust background in cyber security gained from tenure at the IDF, Sarah has consistently driven impactful decision-making and technological advancements throughout their career. Academic credentials include a Master's degree in Condensed Matter Physics from the Weizmann Institute of Science and a Bachelor's degree in Mathematics and Physics from The Hebrew University of Jerusalem. In the episode, Richie and Sarah explore the challenges of data governance, the role of semantic layers in ensuring data trust, the emergence of analytics engineers, the integration of AI in data processes, and much more. Links Mentioned in the Show: EunoConnect with SarahCourse: Responsible AI Data ManagementRelated Episode: How Data Leaders Can Make Data Governance a Priority with Saurabh Gupta, Chief Strategy & Revenue Officer at The Modern Data CompanyRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Welcome to DataFramed Industry Roundups! In this series of episodes, Adel & Richie sit down to discuss the latest and greatest in data & AI. In this episode, we touch upon AI agents for data work, will the full-stack data scientist make a return, old languages making a comeback, Python's increase in performance, what they're both thankful for, and much more. Links Mentioned in the Show Fractal’s Data Science Agent: AryaArticle: What Makes a True AI Agent? Rethinking the Pursuit of AutonomyCassie Kozyrkov on DataFramedTIOBE Index for November 2024Community discussion on FortranTutorial: High Performance Data Manipulation in Python: pandas 2.0 vs. polars New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. We’re often caught chasing the dream of “self-serve” data—a place where data empowers stakeholders to answer their questions without a data expert at every turn. But what does it take to reach that point? How do you shape tools that empower teams to explore and act on data without the usual bottlenecks? And with the growing presence of natural language tools and AI, is true self-service within reach, or is there still more to the journey? Sameer Al-Sakran is the CEO at Metabase, a low-code self-service analytics company. Sameer has a background in both data science and data engineering so he's got a practitioner's perspective as well as executive insight. Previously, he was CTO at Expa and Blackjet, and the founder of SimpleHadoop and Adopilot. In the episode, Richie and Sameer explore self-serve analytics, the evolution of data tools, GenAI vs AI agents, semantic layers, the challenges of implementing self-serve analytics, the problem with data-driven culture, encouraging efficiency in data teams, the parallels between UX and data projects, exciting trends in analytics, and much more. Links Mentioned in the Show: MetabaseConnect with SameerArticles from Metabase on jargon, information budgets, analytics mistakes, and data model mistakesCourse: Introduction to Data CultureRelated Episode: Towards Self-Service Data Engineering with Taylor Brown, Co-Founder and COO at FivetranRewatch Sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. Data is no longer just for coders. With the rise of low-code tools, more people across organizations can access data insights without needing programming skills. But how can companies leverage these tools effectively? And what steps should they take to integrate them into existing workflows while upskilling their teams?  Michael Berthold is CEO and co-founder at KNIME, an open source data analytics company. He has more than 25 years of experience in data science, working in academia, most recently as a full professor at Konstanz University (Germany) and previously at University of California (Berkeley) and Carnegie Mellon, and in industry at Intel’s Neural Network Group, Utopy, and Tripos. Michael has published extensively on data analytics, machine learning, and artificial intelligence. In the episode, Adel and Michael explore low-code data science, the adoption of low-code data tools, the evolution of data science workflows, upskilling, low-code and code collaboration, data literacy, integration with AI and GenAI tools, the future of low-code data tools and much more.  Links Mentioned in the Show: KNIMEConnect with MichaelCode Along: Low-Code Data Science and Analytics with KNIMECourse: Introduction to KNIMERelated Episode: No-Code LLMs In Practice with Birago Jones & Karthik Dinakar, CEO & CTO at Pienso New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Businesses are collecting more data than ever before. But is bigger always better? Many companies are starting to question whether massive datasets and complex infrastructure are truly delivering results or just adding unnecessary costs and complications. How can you make sure your data strategy is aligned with your actual needs? What if focusing on smaller, more manageable datasets could improve your efficiency and save resources, all while delivering the same insights? Ryan Boyd is the Co-Founder & VP, Marketing + DevRel at MotherDuck. Ryan started his career as a software engineer, but since has led DevRel teams for 15+ years at Google, Databricks and Neo4j, where he developed and executed numerous marketing and DevRel programs. Prior to MotherDuck, Ryan worked at Databricks and focussed the team on building an online community during the pandemic, helping to organize the content and experience for an online Data + AI Summit, establishing a regular cadence of video and blog content, launching the Databricks Beacons ambassador program, improving the time to an “aha” moment in the online trial and launching a University Alliance program to help professors teach the latest in data science, machine learning and data engineering. In the episode, Richie and Ryan explore data growth and computation, the data 1%, the small data movement, data storage and usage, the shift to local and hybrid computing, modern data tools, the challenges of big data, transactional vs analytical databases, SQL language enhancements, simple and ergonomic data solutions and much more.  Links Mentioned in the Show: MotherDuckThe Small Data ManifestoConnect with RyanSmall DataSF conferenceRelated Episode: Effective Data Engineering with Liya Aizenberg, Director of Data Engineering at AwayRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Every organization today is exploring generative AI to drive value and push their business forward. But a common pitfall is that AI strategies often don’t align with business objectives, leading companies to chase flashy tools rather than focusing on what truly matters. How can you avoid these traps and ensure your AI efforts are not only innovative but also aligned with real business value?  Leon Gordon, is a leader in data analytics and AI. A current Microsoft Data Platform MVP based in the UK, founder of Onyx Data. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data. Leon is an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor. In the episode, Adel and Leon explore aligning AI with business strategy, building AI use-cases, enterprise AI-agents, AI and data governance, data-driven decision making, key skills for cross-functional teams, AI for automation and augmentation, privacy and AI and much more.  Links Mentioned in the Show: Onyx DataConnect with LeonLeon’s Linkedin Course - How to Build and Execute a Successful Data StrategySkill Track: AI Business FundamentalsRelated Episode: Generative AI in the Enterprise with Steve Holden, Senior Vice President and Head of Single-Family Analytics at Fannie MaeRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved?  Travis Dalton is the President and CEO at Multiplan overseeing the execution of the company's mission and growth strategy. He has 20 years of leadership experience, with a focus on reducing the cost of healthcare, and enabling better outcomes for patients and healthcare providers. Previously, he was a General Manager and Executive VP at Oracle Health. Jocelyn Jiang is the Vice President of Data & Decision Science at MultiPlan, a role she has held since 2023. In her position, she is responsible for leading the data and analytics initiatives that drive the company’s strategic growth and enhance its service offerings in the healthcare sector. Jocelyn brings extensive experience from her previous roles in healthcare and data science, including her time at EPIC Insurance Brokers & Consultants and Aon, where she worked in various capacities focusing on health and welfare consulting and actuarial analysis. In the episode, Richie, Travis and Jocelyn explore the US healthcare system and the industry-specific challenges professionals face, the role of data in healthcare, ML and data science in healthcare, the future potential of healthcare tech, the global application of healthcare data solutions and much more.  Links Mentioned in the Show: MultiplanPlanOptix: Providing Innovative Healthcare Price Transparency   Using a Data Mining Service on Claims Data Can Reveal Significant OverpaymentsConnect with Travis and JocelynCourse: Intro to Data PrivacyRelated Episode: Data & AI for Improving Patient Outcomes with Terry Myerson, CEO at TruvetaRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while maintaining control and transparency? And how can you build excitement and trust around AI within your organization, ensuring that everyone is ready to embrace this new era? Steve Holden is the Senior Vice President and Head of Single-Family Analytics at Fannie Mae, leading a team of data science professionals, supporting loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He is also responsible for all Generative AI initiatives across the enterprise. His team provides real-time analytic solutions that guide thousands of daily business decisions necessary to manage this extensive mortgage portfolio. The team comprises experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design. Holden previously served as Vice President of Credit Portfolio Management Analytics at Fannie Mae. Before joining Fannie Mae in 1999, he held several analytic leadership roles and worked on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics. In the episode Adel and Steve explore opportunities in generative AI, building a GenAI program, use-case prioritization, driving excitement and engagement for an AI-first culture, skills transformation, governance as a competitive advantage, challenges of scaling AI, future trends in AI, and much more.  Links Mentioned in the Show: Fannie MaeSteve’s recent DataCamp Webinar: Bringing Generative AI to the EnterpriseVideo: Andrej Karpathy - [1hr Talk] Intro to Large Language ModelsSkill Track - AI Business FundamentalsRelated Episode: Generative AI at EY with John Thompson, Head of AI at EYRewatch sessions from RADAR: AI Edition Join the DataFramed team! Data Evangelist Data & AI Video Creator New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the same problem: not having an AI strategy and not understanding how to tactically implement AI. There’s a lot to consider around choosing the right project and putting processes and skilled talent in place, not to mention worrying about costs and return on investment. Tathagat Varma is the Global TechOps Leader at Walmart Global Tech. Tathagat is responsible for leading strategic business initiatives, enterprise agile transformation, technical learning and enablement, strategic technical initiatives, startup ecosystem engagement, and internal events across Walmart Global Tech. He also provides support to horizontal technical and internal innovation programs in the company. Starting as a Computer Scientist with DRDO, and with an overall experience of 27 years, Tathagat has played significant technical and leadership roles in establishing and growing organizations like NerdWallet, ChinaSoft International, McAfee, Huawei, Network General, NetScout System, [24]7 Innovations Labs and Yahoo!, and played key engineering roles at Siemens and Philips. In the episode, Richie and Tathagat explore failures in AI adoption, the role of leadership in AI adoption, AI strategy and business objective alignment, investment and timeline for AI projects, identifying starter AI projects, skills for AI success, building a culture of AI adoption, the potential of AI and much more.  Links Mentioned in the Show: Walmart Global TechConnect with Tathagat[Course] Data Governance ConceptsRelated Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at WalmartRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

One of the best applications of data science is that it allows experimentation within any organization at scale. The ability to test a new checkout feature, the color of a button, and analyze whether that improves customer experiences can be truly magical when done correctly. However, doing this at scale means that the entire organization needs to be bought into the experimentation agenda. So how do you do this and how do you make sure this becomes part of your organization’s culture? Amit Mondal is the VP & Head of Digital Analytics & Experimentation at American Express. Throughout his career Amit has been a financial services leader in digital, analytics/data science and risk management, driving digital strategies and investments, while creating a data driven & experimentation first culture for Amex. Amit currently leads a global team of 200+ Data Scientists, Statisticians, Experimenters, Analysts, and Data experts. In the episode, Adel and Amit explore the importance of experimentation at American Express, key components of experimentation strategies, ownership and coordination in experimentation processes, the pillars that feed into a culture of experimentation, frameworks for building successful experiments, robust experiment design, challenges and trends across industries and much more.  Links Mentioned in the Show: American ExpressDecoding Marketing Mix Modeling[Course] A/B Testing in PythonRelated Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at TescoRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Excel often gets unfair criticism from data practitioners, many of us will remember a time when Excel was looked down upon—why would anyone use Excel when we have powerful tools like Python, R, SQL, or BI tools? However,  like it or not, Excel is here to stay, and there’s a meme, bordering on reality, that Excel is carrying a large chunk of the world’s GDP. But when it really comes down to it, can you do data science in Excel? Jordan Goldmeier is an entrepreneur, a consultant, a best-selling author of four books on data, and a digital nomad. He started his career as a data scientist in the defense industry for Booz Allen Hamilton and The Perduco Group, before moving into consultancy with EY, and then teaching people how to use data at Excel TV, Wake Forest University, and now Anarchy Data. He also has a newsletter called The Money Making Machine, and he's on a mission to create 100 entrepreneurs.  In the episode, Adel and Jordan explore excel in data science, excel’s popularity, use cases for Excel in data science, the impact of GenAI on Excel, Power Query and data transformation, advanced Excel features, Excel for prototyping and generating buy-in, the limitations of Excel and what other tools might emerge in its place, and much more.  Links Mentioned in the Show: Data Smart: Using Data Science to Transform Information Into Insight by Jordan Goldmeier[Webinar] Developing a Data Mindset: How to Think, Speak, and Understand Data[Course] Data Analysis in ExcelRelated Episode: Do Spreadsheets Need a Rethink? With Hjalmar Gislason, CEO of GRIDRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Despite GPT, Claude, Gemini, LLama and the other host of LLMs that we have access to, a variety of organizations are still exploring their options when it comes to custom LLMs. Logging in to ChatGPT is easy enough, and so is creating a 'custom' openAI GPT, but what does it take to create a truly custom LLM? When and why might this be useful, and will it be worth the effort? Vincent Granville is a pioneer in the AI and machine learning space, he is Co-Founder of Data Science Central, Founder of MLTechniques.com, former VC-funded executive, author, and patent owner. Vincent’s corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. He is also a former post-doc at Cambridge University and the National Institute of Statistical Sciences. Vincent has published in the Journal of Number Theory, Journal of the Royal Statistical Society, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is the author of multiple books, including “Synthetic Data and Generative AI”. In the episode, Richie and Vincent explore why you might want to create a custom LLM including issues with standard LLMs and benefits of custom LLMs, the development and features of custom LLMs, architecture and technical details, corporate use cases, technical innovations, ethics and legal considerations, and much more.  Links Mentioned in the Show: Read Articles by VincentSynthetic Data and Generative AI by Vincent GranvilleConnect with Vincent on Linkedin[Course] Developing LLM Applications with LangChainRelated Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at PineconeRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business