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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#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.