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 


#28 Organizing Data Science Teams

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone.

Join co-hosts Adel Nehme and Richie Cotton as they delve into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

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Over the past 199 episodes of DataFramed, we’ve heard from people at the forefront of data and AI, and over the past year we’ve constantly looked ahead to the future AI might bring. But all of the technologies and ways of working we’ve witnessed have been built on foundations that were laid decades ago. For our 200th episode, we’re bringing you a special guest and taking a walk down memory lane—to the creation and development of one of the most popular programming languages in the world. Don Chamberlin is renowned as the co-inventor of SQL (Structured Query Language), the predominant database language globally, which he developed with Raymond Boyce in the mid-1970s. Chamberlin's professional career began at IBM Research in Yorktown Heights, New York, following a summer internship there during his academic years. His work on IBM's System R project led to the first SQL implementation and significantly advanced IBM’s relational database technology. His contributions were recognized when he was made an IBM Fellow in 2003 and later a Fellow of the Computer History Museum in 2009 for his pioneering work on SQL and database architectures. Chamberlin also contributed to the development of XQuery, an XML query language, as part of the W3C, which became a W3C Recommendation in January 2007. Additionally, he holds fellowships with ACM and IEEE and is a member of the National Academy of Engineering. In the episode, Richie and Don explore his early career at IBM and the development of his interest in databases alongside Ray Boyce, the database task group (DBTG), the transition to relational databases and the early development of SQL, the commercialization and adoption of SQL, how it became standardized, how it evolved and spread via open source, the future of SQL through NoSQL and SQL++ and much more.  Links Mentioned in the Show:The first-ever journal paper on SQL. SEQUEL: A Structured English Query LanguageDon’s Book: SQL++ for SQL Users: A TutorialSystem R: Relational approach to database managementSQL CoursesSQL Articles, Tutorials and Code-AlongsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch sessions from RADAR: The Analytics Edition
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#200 50 Years of SQL with Don Chamberlin, Computer Scientist and Co-Inventor of SQL

Last year saw the proliferation of countless AI tools and initiatives, many companies looked to find ways where AI could be leveraged to reduce operational costs and pressure wherever possible. 2023 was a year of experimentation for anyone trying to harness AI, but we can’t walk forever. To keep up with the rapidly changing landscape in business, last year’s experiments with AI need to find their feet and allow us to run. But how do we know which initiatives are worth fully investing in? Will your company culture impede the change management that is necessary to fully adopt AI? Sanjay Srivastava is the Chief Digital Strategist at Genpact. He works exclusively with Genpact’s senior client executives and ecosystem technology leaders to mobilize digital transformation at the intersection of cutting-edge technology, data strategy, operating models, and process design. In his previous role as Chief Digital Officer at Genpact, Sanjay built out the company’s offerings in artificial intelligence, data and analytics, automation, and digital technology services. He leads Genpact’s artificial-intelligence-enabled platform that delivers industry-leading governance, integration, and orchestration capabilities across digital transformations. Before joining Genpact, Sanjay was a Silicon Valley serial entrepreneur and built four high-tech startups, each of which was successfully acquired by Akamai, BMC, FIS, and Genpact, respectively. Sanjay also held operating leadership roles at Hewlett Packard, Akamai, and SunGard (now FIS), where he oversaw product management, global sales, engineering, and services businesses. In the episode, Sanjay and Richie cover the shift from experimentation to production seen in the AI space over the past 12 months, the importance of corporate culture in the adoption of AI in a business environment, how AI automation is revolutionizing business processes at GENPACT, how change management contributes to how we leverage AI tools at work, adapting skill development pathways to make the most out of AI, how AI implementation changes depending on the size of your organization, future opportunities for AI to change industries and much more.  Links Mentioned in the Show:Genpact[Course] Implementing AI Solutions in BusinessArticle: AI adoption accelerates as enterprise PoCs show productivity gainsRelated Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and FuturistRewatch sessions from RADAR: The Analytics Edition
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#199 Creating an AI-First Culture with Sanjay Srivastava, Chief Digital Strategist at Genpact

There aren’t many retail giants like Walmart. In fact, there are none. The multinational generates 650bn in revenue, (including 50bn in eCommerce)—the highest revenue of any retailer globally. With over 10,000 stores worldwide and a constantly evolving product line, Walmart’s data & AI function has a lot to contend with when it comes to customer experience, demand forecasting, supply chain optimization and where to use AI effectively. So how do they do it? What can we learn from one of the most successful and well-known organizations on the planet? Swati Kirti is a Senior Director of Data Science, leading the AI/ML charter for Walmart Global Tech’s international business in Canada, Mexico, Central America, Chile, China, and South Africa. She is responsible for building AI/ML models and products to enable automation and data-driven decisions, powering superior customer experience and realizing value for omnichannel international businesses across e-commerce, stores, supply chain, and merchandising. In the episode, Swati and Richie explore the role of data and AI at Walmart, how the data and AI teams operate under Swati’s supervision, how Walmart improves customer experience through the use of data, supply chain optimization, demand forecasting, retail-specific data challenges, scaling AI solutions, innovation in retail through AI and much more.  Links Mentioned in the Show:Article - Walmart’s Generative AI search puts more time back in customers' handsWalmart Global Tech[Course] Implementing AI Solutions in BusinessRelated Episode: How Generative AI is Changing Business and Society with Bernard Marr, AI Advisor, Best-Selling Author, and FuturistRewatch sessions from RADAR: The Analytics 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


#198 How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at Walmart

Generative AI has had a wide range of uses, but some of its strongest use cases are in coding and programming. One of the companies that has been leading the way in AI-assisted programming has been GitHub with GitHub CoPilot. Many software engineering teams now have tools like CoPilot embedded into their workflows, but what does this mean for the future of programming? Kyle Daigle is the COO of GitHub, leading the strategic initiatives, operations, and innovation of the world's largest platform for software development and collaboration. With over 10 years of experience at GitHub, Kyle has a deep understanding of the needs and challenges of developers and the ecosystem they work in. In the episode, Adel and Kyle explore Kyle’s journey into development and AI, how he became the COO at GitHub, GitHub’s approach to AI, the impact of CoPilot on software development, how AI tools are adopted by software developers, the future of programming and AI’s role within it, the risks and challenges associated with the adoption of AI coding tools, the broader implications tools like CoPilot might have and much more.  Links Mentioned in the Show:GitHub CoPilotKyle on GitHub[Code Along] Pair Programming with GitHub Copilot[Course] GitHub ConceptsRelated Episode: What to Expect from AI in 2024 with Craig S. Smith, Host of the Eye on A.I PodcastRewatch sessions from RADAR: The Analytics Edition
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#197 The Future of Programming with Kyle Daigle, COO at GitHub

Since the launch of ChatGPT, one of the trending terms outside of ChatGPT itself has been prompt engineering. This act of carefully crafting your instructions is treated as alchemy by some and science by others. So what makes an effective prompt? Alex Banks has been building and scaling AI products since 2021. He writes Sunday Signal, a newsletter offering a blend of AI advancements and broader thought-provoking insights. His expertise extends to social media platforms on X/Twitter and LinkedIn, where he educates a diverse audience on leveraging AI to enhance productivity and transform daily life. In the episode, Alex and Adel cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, strategies for better prompting, chain of thought prompting, prompt engineering as a skill and career path, building your own AI tools rather than using consumer AI products, AI literacy, the future of LLMs and much more.  Links Mentioned in the Show:[Alex’s Free Course on DataCamp] Understanding Prompt EngineeringSunday SignalPrinciples by Ray Dalio: Life and WorkRelated Episode: [DataFramed AI Series #1] ChatGPT and the OpenAI Developer EcosystemRewatch sessions from RADAR: The Analytics 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


#28 Organizing Data Science Teams

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