Half Stack Data Science

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.ioYou can also find Richard and Niko on LinkedIn.—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 conversation, Simon Willison, creator of Datasette and co-creator of Django, discusses his passion for building open-source tools for data journalism. He shares insights on blending AI with coding to empower journalists and the benefits of local large language models. Simon also dives into the transformation AI brings to programming and education, stressing the importance of foundational knowledge. Plus, he shares playful interactions with AI, revealing its creative potential and limitations. Expect intriguing thoughts on technology and journalism!

Scott Hanselman, Vice President of Developer Community at Microsoft, shares his insights on AI, where he emphasizes the importance of ethical considerations and maintaining foundational skills in technology. He discusses the risks of over-reliance on tools like ChatGPT and the intriguing concept of 'dark matter developers'—those behind the scenes innovating daily. The conversation also touches on local innovations, such as using AI in 3D printing for amputees and the privacy-first approach of tools like Storygraph. Overall, it's a thought-provoking dive into the intersection of technology and society.

In this finale, an engaging discussion highlights the knowledge gap in data education and the need for real-world project experience. The importance of categorical data and ethical sourcing is explored, alongside the challenges of translating theoretical knowledge into practical applications. Navigating data analysis methodologies is emphasized as critical for effective problem-solving. Furthermore, the hosts reflect on their podcast journey and tease upcoming insights on AI, making for a thought-provoking conclusion to the season.

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