#49 Data Science Tool Building

Hugo speaks with Wes McKinney, creator of the pandas project for data analysis tools in Python and author of Python for Data Analysis, among many other things. Wes and Hugo talk about data science tool building, what it took to get pandas off the ground and how he approaches building “human interfaces to data” to make individuals more productive. On top of this, they’ll talk about the future of data science tooling, including the Apache arrow project and how it can facilitate this future, the importance of DataFrames that are portable between programming languages and building tools that facilitate data analysis work in the big data limit. Pandas initially arose from Wes noticing that people were nowhere near as productive as they could be due to lack of tooling & the projects he’s working on today, which they’ll discuss, arise from the same place and present a bold vision for the future.LINKS FROM THE SHOWDATAFRAMED SURVEY

• DataFramed Survey (take it so that we can make an even better podcast for you)

DATAFRAMED GUEST SUGGESTIONS

• DataFramed Guest Suggestions (who do you want to hear on Season 2?)

FROM THE INTERVIEW

• Wes on Twitter
• Roads and Bridges: The Unseen Labor Behind Our Digital Infrastructure by Nadia Eghbal
• pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
• Ursa Labs

FROM THE SEGMENTS

Data Science Best Practices (with Ben Skrainka ~17:10)

• To Explain or To Predict? (By Galit Shmueli)
• Statistical Modeling: The Two Cultures (By Leo Breiman)
• The Book of Why (By Judea Pearl & Dana Mackenzie)

Studies in Interpretability (with Peadar Coyle at ~39:00)

• Modelling Loss Curves in Insurance with RStan (By Mick Cooney)
• Lime: Explaining the predictions of any machine learning classifier 
• Probabilistic Programming Primer

Original music and sounds by The Sticks.

Episode source