25 episodes
Podcast of Small Differences Otis Anderson
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- Business
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5.0 • 6 Ratings
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A podcast about data science, but also just about work. Hosted by Otis Anderson and Ian Blumenfeld, who have both data scienced for a long time. Hear from some data humans who have had some successes and some failures. We know, this isn't easy
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Last Lunch
This is a bulk re-publish of old, lost episodes.
Ian and Otis talk about recent sales in the BI sector, the Toronto Raptors. We learn that Otis has tendency towards overly literal descriptions. In the last few minutes we announce that the show will be on hiatus for a while. -
Memes and Video Games
This is a bulk re-publish of old, lost episodes.
Otis and Ian have no guests this week. They discuss workplace culture and career development. -
Tools to Make More Tools
Lauren Chircus drops by to discuss developing tools for data scientists. We also talk about what a data strategy gets you that tooling can't.
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What to Expect When You're Execing
Drew Dillon has done product, executive things. He give you some insight into why it can be hard to see across an organization, why PMs lag on data.
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Weird, Small Corners
Dave Guarino stops by to talk about working with government, political economy and living a life dedicated to not getting stuck on a local maxima.
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Where Are My Pins?!
Otis wants you to stop calling whole pieces of recommendation software "algorithms". Stichfix has an interesting model of data science and it is discussed. There's a bit about Top Chef.
Customer Reviews
Ok, so Otis is my brother, but...
If you’re into data analytics, this is for you. It’s a nice slice of practical advice and the thought process behind what they do what they do. For a person who is NOT into analytics, I still find it broadens my perspective on a host of other issues. Good stuff all around.
Resource for transitioning physicists
This is the honest and nerdy conversation about data in industry that I need. There are tons of great tutorials for learning how to do things. But as an astrophysicist transitioning into data science at a startup, I often have a hard time figuring out what is worthwhile to learn. It’s fun to listen to this show for tidbits and go off exploring on my own from there. And as a nerd who enjoys being right, I have also enjoyed the vindication in hearing you two echo my feelings on GUIs, notebooks, and “p-hacking.”
Sorry I don’t have any salty gripes about p-values for ya!