My Podcast routine

I have been a fan of listening to podcasts for a long time now. I started this habit back in 2012 with my first iPhone. Being able to pick and choose radio style informational shows was perfect for me. I do not remember if cost was a factor but streaming music wasn’t big “when” I came from. Since then, I must have gone through many different shows, some of them coming to end of life, and some falling out of love. Here are the podcasts that I currently listen to (or recently finished listening to) as of fall 2020. I do not think this habit is going die anytime soon. I perfer to listen to the episodes at x1.5 speed (and sometimes at x2)

Here is my list of current podcasts with a short description. These are not reviews or endorsements and r untimes are rough estimates.

Current Affairs

The Economist: After 2018 The Economist divided the different categories of shows into their own podcast. The meta podcast is still available but I now listen to these sub podcasts.

• The Intelligence: This is mostly about current affairs in about 2-3 segments. It runs for about 25 minutes and out every weekday and this is how I start my day.

• Money Talks: Weekly show about world of finance, central banks, world street, and economy in general. Also runs for about 25 minutes and is out weekly.

• Editor’s Picks: Another weekly show where they read 2-3 stories from the magazine. Gives the taste of magazine stories in about 25 minutes.

• Babbage: This weekly show is about science and technology. The topic ranges from AI and Machine Learning, medicine, physics, etc. to big tech, pharmaceutical, and evolution. Runtime: 25 minutes.

• The World Ahead: This is a monthly show where they discuss how the world might look like in the future. Hosts discuss about impact of recent activities to the future, or provide commentary with reference to how certain historical shapes our present and future. Runtime: 25 mins.

Python

• Talk Python to me: A weekly show where the host @mkennedy invites people from the world of programming (mostly python) and talk about python projects or use case. Runtime: 1 Hour

• Python Bytes: A weekly “python” news show where @mkennedy and @brianokken talk about what is happening in the world of python and tech in general. Topic includes new python libraries, cool projects, dev tools, etc. Runtime: ~30 minutes

• Test and Code: This is a weekly show about pretty much all thing programming. @brianokken covers things from writing unit test, other testing in programming, python, programming career, his book ;). Similar to its theme and topic its runtime also varies from ~10 minutes to over 1 Hour.

• The Real Python Podcast: This one is relatively new and it is covers both latest trends in python and interview with pythonistas. They also highlight some Real Python articles (free) and courses (premium). Runtime ~ 1 hours (most run between 50 to 90 minutes)

Data

• Data - Software Engineering Daily: I listen to the “Data” portion of the Software Engineering Daily podcast. This show is about software/data platforms in general and the host interviews software/data engineers about the industry, their platform, and their outlook on the industry. The main podcast runs every weekday with the episode tagged “data” about once a week. Runtime ~40-60 minutes

• Data Engineering Podcast: I listen to this weekly data engineering podcasts to get a deeeper dive and understanding of some of the novel approaches in data engieering, management, and analytics. @TobiasMacey interivews different founders and senior engineers on how they build the data infrastructure team or how they built the platform. Runtime ~ 40-60 minutes

• Data Skeptic: On this weekly show Kyle brings in guests to talk about their work using data science. He also has monologue on certain topic (Natural Language, Interpretability, etc.) that runs like a season. In the earlier episodes, the also tries to explain some data science concept to his wife (and sometimes to their pet parrot). Runtime 30-40 minutes

• The Data Exchange: This is a weekly podcast where the host Ben Lorica speaks with AI and machine learning researchers and practitioner and also talks about new tools and technology in the space. I consider this as a continuation of the host’s previous show O’Reilly Data Show. Runtime 30-40 minutes.

• Not So Standard Deviation: This is a light conversation style show where I feel like listening at two friend’s (@hspter and @rdpeng ) conversation. The conversation centres around the topic of data science, R, and coffee. On a normal schedule the shows run twice monthly. Runtime ~1 Hour

• Linear Digression: This show has reached its end of life but was a great data science show during its running. In this show, [Katie] an astrophysicist and a data scientist explains [Ben] a software engineer, concetps and algorithm used in data science often starting with a pun. They also talk about carrer in data science and towards the end bring other guests to talk about data science in non technical setting. Most episodes can be used as on the go machine learning algorithm refresher. Runtime 20-30 minutes

Some shoutouts: Here is a list of some other podcasts that I listen when I am caught up with my subscriptions or are no longer producing new episodes

• Data Framed: Interviews with data scientists hosted by Hugo. Podcast ended after the Data Camp Sexual Harassment Incident
• Ladybug Podcast: Women voices in Software Development podcasts (and industry) created by women in tech for everyone. Available on Spotify. On-going
• Partially Derivative: General Data Science around us
• Becoming A Data Scientist: The host, Renee, interviews people in data science and how they became one while transitioning from a SQL analyst to a data scientist herself. 17 Episodes most of them in 2016.
• Teaching Python: Two school-teachers teaching python to middle-schoolers where one is transitioning to teaching from software engineering and the other learning programming as a teacher
• The Programmer’s Toolbox: Introduces software engineering, concepts, and skills needed to become a software engineer. One season with around 11 episodes available.
• dats n stats: Statistics and data science related. Less than 10 episodes available.
• The R-Podcast: Entirely about R. Talks about R packages, frameworks, people, and conferences.
• if/else: Debate style show on two technology that could substitute one another and the use case for each of them. About 6 episodes total
• O’Reilly Data Show: Old version of The Data Exchange
• Tasting Menu: The economists’ sample stories (replaced by the editor’s picks)

As you can see, I listen to a lot of technical podcasts. iOS Podcasts App is still where I get my podcast . In particular my podcast interests are current affairs, python, R, data science and machine learning, data management, and general technology.

Disclaimer: This is not an endorsement of any of the shows or the people invloved in making them and I do not benefit from this post.