In this episode, we spoke to Kirsten Hill.

Dr Kirsten Hill is a researcher, writer, and the founder of bisque — a survey platform that actually cares if you get good data. She has a Ph.D. from the University of Pennsylvania, has led research for the Bill & Melinda Gates Foundation, and has worked with over 100 mission-driven teams on measurement & evaluation. This past year, she authored her first book, Ask Better Questions, a top release on survey design — and realized that writing the book wasn’t enough. If she really wanted to change how people do research, she had to build the tools that would make it easier to do it right. With real-time AI feedback, intuitive design, and built-in best practices, bisque makes it easy to create surveys that get you the answers you actually need.

We spoke to Kirsten about how and why surveys are often bad, why she believes the major survey platforms aren't fit for purpose, why she's building her own survey platform "bisque", how she's using Large Language Models to develop a working prototype, and what advice she has for other non-programmers who want to build things with AI.

You can find her on:

• LinkedIn @kirstenleehill
• TikTok @kirstenleehill
• Substack kirstenleehill.substack.com

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David’s book, The Well-Grounded Data Analyst is out! ⁠https://www.manning.com/books/the-well-grounded-data-analyst⁠

If you want to find out more, we have a whole episode about it: ⁠https://open.spotify.com/episode/5D0iDtQRh3tWiIhokrjz3x?si=AiX6YyRET16lnzXDlvdcfw

In this episode, we spoke to Richard Makara and Niko Korvenlaita who are CEO and CTO of reconfigured.

reconfigured is a journal app that makes it incredibly easy to capture and retrieve on-going notes, thoughts & insights. Richard is the CEO and he has led growth and built go-to-market data systems in high-growth scale-ups. Niko is a second time founder and the CTO. He has been building end-to-end machine learning applications since 2014 and has worked with Natural Language Processing since 2017.

We spoke to Richard and Niko about the common problems data professionals face, how data people create knowledge faster than they can capture it and what challenges that creates, why reducing the cost of note-taking to zero is so important for data work, how AI can help with the retrieval and structuring of ideas, and how to use AI in a product in a thoughtful and meaningful way.

Please enjoy our conversation with the team behind reconfigured!

Find out more about reconfigured here: https://reconfigured.io

You can also find Richard and Niko on LinkedIn.

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David’s book, The Well-Grounded Data Analyst is out! ⁠https://www.manning.com/books/the-well-grounded-data-analyst⁠

If you want to find out more, we have a whole episode about it: ⁠https://open.spotify.com/episode/5D0iDtQRh3tWiIhokrjz3x?si=AiX6YyRET16lnzXDlvdcfw

In this episode we talked to Simon Willison.

Simon is the creator of Datasette, an open source tool for exploring and publishing data. He currently works full-time building open source tools for data journalism, built around Datasette and SQLite.

Prior to becoming an independent open source developer, Simon was an engineering director at Eventbrite. Simon joined Eventbrite through their acquisition of Lanyrd, a Y Combinator funded company he co-founded in 2010.

He is a co-creator of the Django Web Framework, and has been blogging about web development and programming since 2002 at simonwillison.net

We talked to Simon about his goal of building tools for data journalists, what he's learned about tinkering with, and writing about, AI models for years, his excitement about their code-generating capabilities, how to get the most out of all of these tools, and what generative AI tools have to do with pelicans.

You can find Simon at https://simonwillison.net

Please enjoy our conversation with Simon Willison!

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David's book, The Well-Grounded Data Analyst is out! ⁠https://www.manning.com/books/the-well-grounded-data-analyst⁠

If you want to find out more, we have a whole episode about it: ⁠https://open.spotify.com/episode/5D0iDtQRh3tWiIhokrjz3x?si=AiX6YyRET16lnzXDlvdcfw

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

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David's book, The Well-Grounded Data Analyst is out! https://www.manning.com/books/the-well-grounded-data-analyst

If 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-analyst

Read more about David's work here: https://davidasboth.com/

If you're an aspiring analyst and want to test your existing data skills on real-world problems while learning both new skills and a framework you can apply to any problem, David's book Solve Any Data Analysis Problem is available on the Manning website through the Early Access Program and it's out in print later this year.

Find it at https://www.manning.com/books/solve-any-data-analysis-problem

In this episode, we welcome back a former guest, Andrea Jones-Rooy, Ph.D.

Andrea is the founder of Data Thinking, an organization dedicated to promoting worldwide data literacy and public involvement in data science. Previously, she was the Director of Undergraduate Studies and Visiting Associate Professor at the NYU Center for Data Science, where she developed and taught their flagship course, Data Science for Everyone, as well as advanced courses on Natural Language Processing. Dr. Jones-Rooy believes everyone has a role to play in data science, and you (yes, you!) can get involved at DataScienceNeedsYou.com!

We spoke to Andrea about how to teach people the scientific method and to deal with uncertainty, the impact of generative AI on teaching these topics, whether these skills should be called "data thinking" instead of "data science", and how you even convince people these are the skills worth learning.

Find Andrea at https://datascienceneedsyou.com or on social media platforms as @jonesrooy

A reminder that David's book, Solve Any Data Analysis Problem, is out later this year and you can already buy it and read it in its draft form as part of Manning's Early Access Program. If you want to practise your data skills on real world problems and learn a reusable framework to use on any project in the future, this book is for you.

Find out more here: ⁠https://www.manning.com/books/solve-any-data-analysis-problem⁠

Now onto today's episode.

In this episode, we spoke to Richard McElreath.

Richard is an anthropologist focused on the role of culture in human evolution and adaptation. He is currently the Director of the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. A major focus of the department is integrating theory with data analysis and study design, and Richard spends much of his time supporting his colleagues in that way. He is the author of Statistical Rethinking, a popular Bayesian statistics textbook and video course.

We spoke to Richard about the state of scientific research, parallels between the problems in scientific research and doing data analysis in the business world, and to quote Richard, how, if we are very careful and try very hard, we might not completely mislead ourselves.

Richard's departmental page: https://www.eva.mpg.de/ecology/staff/richard-mcelreath

Richard's blog: https://elevanth.org/blog

Richard on Twitter: https://twitter.com/rlmcelreath

A reminder that David's book, Solve Any Data Analysis Problem, is out later this year and you can already buy it and read it in its draft form as part of Manning's Early Access Program. If you want to practise your data skills on real world problems and learn a reusable framework to use on any project in the future, this book is for you.

Find out more here: https://www.manning.com/books/solve-any-data-analysis-problem

Now onto today's episode. We're continuing our series of conversations about data education and in this episode we spoke to Richie Cotton.

Richie is a data evangelist at DataCamp. He started his career as a data scientist, working in industries from chemical health and safety to debt collection to proteomics. After joining DataCamp in 2016, he switched to teaching data and AI skills. He has created ten courses on data science that have been taken by over 700k learners, and worked with instructors to create over 50 courses that have been taken by millions of learners. Richie has also written two books and R programming, Learning R and Testing R Code.

In his current role, Richie hosts the DataFramed podcast and runs the DataCamp webinar program, as well as creating tutorials and cheat sheets for data and AI skills.

We spoke to Richie about how DataCamp's offering and focus has changed over time to meet market demands, with some inevitable comments about Python vs R, what the impact of generative AI has been on data education, and what the future holds.

You can find Richie and his work on various parts of the internet:

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).

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In today's episode, we talked to Kat Greenbrook. Kat is a Data Storyteller from Aotearoa, New Zealand. She is a consultant,

Her book, The Data Storyteller's Handbook is out now! You can find Kat and the book at https://www.roguepenguin.co.nz

First of all, 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).

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In this episode, we talked to James Cotton, co-founder of iO-Sphere.

Canadian originally, James has been working in the UK for the last 10 years, always in analytics, pricing, and data science. After a short stint in insurance he was at hotels.com for 3.5 years always working in customer analytics and marketing analytics. He then went to worldremit – a large uk fintech company that’s sort of like a digital western union. There he built out a team, growing it significantly. Over the past 7 or 8 years he’s hired dozens and dozens of data professionals of all levels – clearly seeing the gap between existing data training programmes and courses and the real need in industry.

James is one of the founders of iO-Sphere, which was created in order to close that gap with actually useful, practical, training. They also fund all the training of everyone that comes onto the programme – helping to lower financial barriers to accessing high quality training and these careers.

We talked to James about the skill gap between training and the real world, why no one has thought to close that gap in the way io-Sphere have, whether standardisation makes sense for the analytics industry, and of course where AI fits into all this.

Find out more about iO-Sphere here: https://io-sphere.io

You can find James on LinkedIn: https://www.linkedin.com/in/jccotton