There is a podcast out there for everyone, and in some cases, many. I have discovered that there are more than a few podcasts written by and for people like me — unashamed data nerds. Here are my favorites.
Data Skeptic is one of the most popular podcasts in the industry and my go-to data-related podcast.
Its episodes range from 15 minutes to the occasional hour-long deep dive. I would guesstimate that the average length falls around 25 minutes — perfect for a quick commute or something to listen to while you make breakfast. I would recommend Data Skeptic, in particular, to prospective MSA students (especially those coming to the program from a non-STEM field!).
Kyle Polich, the show’s host, earned his BS in computer science and MS in artificial intelligence from the University of Illinois at Chicago. In every episode, Polich approaches — with a healthy dose of scientific skepticism — topics in data science, statistics, machine learning, and artificial intelligence.
Unlike other podcasts, every episode of Data Skeptic does not follow a formula. Some episodes feature interviews with industry professionals and innovators (e.g., Feather — a conversation with Hadley Wickham!); others introduce new tools or strategies for handling data. Some episodes are structured like mini-lessons (e.g., What is Heteroskedasticity?), and other episodes are light-hearted explorations of subjects that appeal to data nerds (e.g., Scientific Studies of People’s Relationship to Music and Mapping Dialects with Twitter Data).
Most episodes of Data Skeptic can be appreciated out of order at your leisure — feel free to jump around and explore the extensive archive of episodes on the Data Skeptic site. Data Skeptic is also available on Apple Podcasts, Google Play, and Stitcher.
Hugo Bowne-Anderson is the podcast’s host and producer as well as a data scientist, educator, and amateur stand-up comedian. Each of the 50+ episodes includes a roughly one-hour-long discussion with an industry professional. Bowne-Anderson’s light-hearted personality (and interest in the promotion of data and AI literacy) helps each interview feel more like a friendly conversation. The episodic structure means you can pick and choose the topics and guests most interesting to you.
The podcast attempts to shine a light on the question “What is Data Science?” by considering what data science can do, and the problems data science professionals are trying to solve through their work. Some of my favorite episodes are Pharmaceuticals and Data Science (with Max Kuhn), Data Security, Data Privacy, and the GDPR (with Katharine Jarmul), and The Credibility Crisis in Data Science (with Skipper Seabold).
Because of its educational nature and variety of topics and perspectives, I recommend this podcast to current MSA students and other individuals looking for jobs in the data science realm. Not only is it an excellent means to learn more about the field, but it’s also a great introduction to the community and the different ways companies use data.
Data Stories is unique in the realm of data-related podcasts because it narrows in on the beautiful products of nitty-gritty data analyses: data visualizations. This podcast is hosted by the dynamic duo of Enrico Bertini — a respected researcher in the field of data visualization with a background in computer science — and Moritz Stefaner — a globetrotting “independent ‘Truth & Beauty Operator’ on the crossroads of data visualization, user interface design and information aesthetics.”
Since 2012, they’ve recorded more than 130 episodes. Most episodes feature a conversation between Bertini, Stefaner, and a special guest (this is the show’s standard formula, but there are a few exceptions). Sometimes, they discuss their guest’s projects or work, but occasionally their exchanges are less-specific and address the broader spectrum of data visualizations or issues in the field. One of my favorite episodes is Ep. 104: Visualization Literacy In Elementary School with Basak Alper and Nathalie Riche, which discusses the promotion of data and visualization literacy.
These episodes spark ideas about new ways of visualizing data, and forces listeners to consider aspects of data visualization — opportunities and potential pitfalls — that they otherwise might not. I would recommend this podcast to anyone who appreciates exciting data science discussions and the potential applications of data visualization advancements.
Need more? Check out three more podcast recommendations from MSA alumni Carlos Blancarte.
This article was originally published on Meg Malone’s blog.