Season 4, all about AI, has launched! In the first episode of the new season we talked to Scott Hanselman.Scott is a programmer, teacher, and speaker. He works out of his home office in Portland, Oregon for Microsoft as the Vice President of Developer Community. He works on .NET, Open Source, and the Azure Cloud Developer Experience, but he also blogs about technology, culture, gadgets, inclusion, code, the web, where we're going and where we've been, and has a number of podcasts and a YouTube channel.We talked to Scott about his role at Microsoft and how he still loves to build things. We talked about AI as a brand, the risks of relying too much on tools like ChatGPT, and how developers can use these technologies meaningfully. We also discussed one of our favourite topics on the podcast, dark matter developers, a term we originally learned from a blog post by Scott himself. See episode 4, Getting Data in our first season for even more about this term.Please enjoy our conversation with Scott Hanselman!You can find Scott at https://www.hanselman.com--David's book, The Well-Grounded Data Analyst is out! https://www.manning.com/books/the-well-grounded-data-analystIf you want to find out more, we have a whole episode about it: https://open.spotify.com/episode/5D0iDtQRh3tWiIhokrjz3x?si=AiX6YyRET16lnzXDlvdcfw

In this episode, Shaun interviews David about his new book, The Well-Grounded Data Analyst.We talk about the data skill gap between the classroom and the real world that made David want to write this book, the importance of tackling real-world projects for further learning, and the underlying methodology of the book that helps you learn to solve any data problem.If you listen to the end, we also tease our upcoming Season 4 of the podcast.Get your copy here: https://www.manning.com/books/the-well-grounded-data-analystRead more about David's work here: https://davidasboth.com/

Andrea Jones-Rooy discusses teaching the scientific method, managing uncertainty, and advocating for data literacy. Topics include rebranding 'data science' as 'data thinking', challenges in teaching data science, the impact of AI, and promoting problem-solving skills over technology reliance.

Richard McElreath, an anthropologist and author of 'Statistical Rethinking,' discusses the importance of integrating theory with data analysis to avoid self-deception in scientific research. He emphasizes transparency, collaboration, and ethical conduct in academia. The podcast explores the significance of generative theory in data analysis, the evolution of scientific knowledge, and the intersection of AI, art, and ethics. Richard also shares insights on utilizing Twitter for collaboration and maintaining a professional web presence.

Richie Cotton, a data education market expert, discusses DataCamp's evolution to meet market demands, Python vs R, generative AI impact on data education, and future predictions. The conversation also covers the shift towards basic data skills, importance of SQL, automation in business, Analytics Engineers role, MLOps, and the use of generative AI in learning.

A reminder that David's book Solve Any Data Analysis Problem is available in Manning's Early Access Program! You can read more about it here: ⁠https://www.manning.com/books/solve-any-data-analysis-problem⁠ (if it's not on offer when you're there, you can get 35% off with the code au35asb). ----------------------------------------------- In today's episode, we talked to Kat Greenbrook. Kat is a Data Storyteller from Aotearoa, New Zealand. She is a consultant, workshop facilitator, industry speaker, and founder of the data storytelling company Rogue Penguin. With a unique blend of science, business, and design, she empowers data professionals to communicate data effectively through storytelling. Her book, The Data Storyteller's Handbook is out now! You can find Kat and the book at https://www.roguepenguin.co.nz

James Cotton, co-founder of iO-Sphere, discusses the gap between data training and industry needs. iO-Sphere provides practical, funded training to bridge this divide. They focus on hands-on learning and personalized assessments for effective skill development in data analytics.

In this episode, we continued our season of talking to data educators and spoke to Laszlo Sragner. Laszlo runs Hypergolic, a boutique consultancy in London specialising in Machine Learning Product Management. Formerly, he was Head of Data Science at Arkera, a fintech startup in London, where he built market intelligence products with Natural Language Processing for Tier 1 investment banks and hedge funds. Before that, Laszlo worked in mobile gaming for King Digital (makers of Candy Crush), specialising in player behaviour and monetisation. He started his career as a quant researcher implementing trading strategies at multiple investment managers. We talked to Laszlo about consulting in the area of machine learning, what data scientists can learn from software engineers, how to upskill data scientists to be better programmers and make programming their craft, and of course how AI tools will impact all of this. You can find Laszlo online: On LinkedIn: https://www.linkedin.com/in/laszlosragner In the Code Quality 4 Data Science community: https://cq4ds.com Laszlo's company, Hypergolic: https://hypergolic.co.uk Laszlo's newsletter, "Deliberate Machine Learning": https://laszlo.substack.com

Continuing our series of conversations with data educators, in this episode we spoke to Valerie Logan. Founding The Data Lodge in 2019, Valerie is as committed to data literacy as it gets.  She believes that in today's digital society, data literacy is not just a work skill- it's a life skill.  With advisory services, train-the-trainer bootcamps, an extensive resource library and community services at The Data Lodge, Valerie is certifying the world’s first Data Literacy Program Leads across commercial, nonprofit and public sectors. In 2022, The Data Lodge was recognized by CDO Magazine as one of the "Top 25 Data Startups to Watch in 2022".  Valerie has more than 28 years of experience, including two decades of global consulting across industries, and five years of applied experience in the telecommunications industry at both field and enterprise levels. She holds a B.S. in Math from SUNY College at Buffalo and an M.S. in Applied Math with a concentration in Operations Research from New Mexico State. We talked to Valerie about how literacy in data is like literacy in any language, how to spread data literacy effectively in a business, what are some obstacles to doing this, how you measure the success of a data literacy program, and of course the effect of the emergence of AI on all of the above. Valerie even made a custom Scrabble board just for this episode, you can see that on our website halfstackdatascience.com If you enjoy our podcast, please consider rating and reviewing it on Apple, Spotify, or wherever you listen to podcasts.

In this episode of Half Stack Data Science we continue our season 3 all about data science education, with a conversation with Matt Harrison. Matt stands as a prominent figure in the Python and data science community. A Stanford Computer Science alumnus, he's made significant contributions through his best-selling books, which include titles like "Effective Pandas", "Effective XGBoost", "Machine Learning Pocket Reference", and "Illustrated Guide to Learning Python 3." Beyond authorship, Matt has shared his expertise at major corporations such as Netflix and NASA, as well as academic institutions like Stanford, the University of Utah, and BYU. With a Python journey beginning in 2000, he's equipped thousands with vital skills, both online and in-person. He runs MetaSnake, a Python and Data training company. We talked to Matt about the pushback he gets whenever he posts code online, what we all think of Excel’s newly announced Python integration, how ChatGPT has affected our work, and whether cooking is a good metaphor for programming.