THIS IS AN UNFINISHED DRAFT

TL;DR

Staying current is not easy. Recommendations for various topics, formats and people below.

Introduction

Have you spent a lot of time carefully avoiding clicking any click-bait-y articles to make sure you don't damage the perfectly curated Google Chrome link recommendations that you're getting right now? Or on Twitter?

Well, maybe that cat video you clicked on once is the reason why you never got to hear about Casual Inference podcast. This article should fix that.

Below are my go-to resources and people that I follow on regular basis.

Recommendations

Twitter

• Sean J Taylor

• Richard McElearth

• Andrew gelman / Stats blog

• Aki Vehtari

• Jake VanderPlas

• Sebastian Rashka

• Kevin P. Murphy

• Susan Athey

• Jeremy Howard - Practical deep learning, nbdev, various programming languages

• Logan Kilpatrick

• Julia core team... TODO

Newsletters, Blogs, Patreons

• M. Betancourt (Patreon, twitter, github) - bayesian case studies, hamiltonian monte

• Bogumil Kaminski - each week

• Erik Engheim

• Ben Stencil - Interesting reflections on the world of data analytics and its impact on decision making (link)

• Chip Huyen - ML Ops, also wrote a great book Designing Machine Learning Systems

• Eugene Yan - A mix of observations and thorough research on various ML topics link

• Batch by Andrew Ng

• Mode / Analytics dispatch

• Data Elixir

• Davis Blalock

• TDS from Medium.com

• Julia Computing (monthly)

• Julia blog aggregator

• ? Julia Forem

Podcasts

• Epidemiology / Causal Inference

  • Serious EPI - Podcasts dedicated to causal inference methods. The second series goes through the Modern Epidemiology book chapter by chapter, so you can save yourself a 1000-pages-worth of reading

  • Casual Inference - Light hearted and very casual but still dedicated to Causal Inference methods (eg, there is an episode with Judea Pearl on personalized medicine)

  • Free associations - Journal club for public health topics. Excellent resource for anyone learning how to apply causal inference to real world problems

• Bayesian Inference

  • Learning Bayesian Stats - Great overview of all the different use cases of Bayesian stats across industries and academic disciplines. Produced by one of the core contributors of PyMC and a member of PyMC Labs

  • Betancourting Disaster - Interesting topics around Bayesian stats

  • Dats n Stats

• ML

  • TWIML - Interesting news and stories from all around the ML scene

  • The world of DaaS - While it started focused on DaaS business, it has branched out recently

  • data sceptic (selective)

  • towards data science - Great podcast, especially the earlier episodes (like the interview of the medium.com data science team)

  • Robot Brains

  • Deepmind the podcast

• Data-driven strategies, Change Managemwnt

  • The Right Track (Avvo)

  • data chief

  • data framed

• Python

  • talk python to me - Great interview and I always learn about new packages or changes in the ecosystem

  • test n code - There is never enough testing!

• Julia

  • talk julia - Great riff on Talk Python to me but for Julia language. They also interviewed a few key people in the Julia scene, so it’s nice to learn more about them (There is a youtube stream as well)

  • juliacon - stream from the 2020 JuliaCon. There is also a youtube stream

• Marketing

  • growth marketing toolbox

  • social media marketing - Great tips, review of various tools and practises

• Other

  • Economist

  • Choiseology

  • Compiler - A mixed bag of tech topics from the Redhat team

  • Acquired - The history of the business and brands you know like you’ve never heard before. Incredibly well researched

Youtube

• Julia for talented amateurs

• Julia Language channel (I'd recommend to watch the most popular videos and past JuliaCons)

• PyData (selective)

Other

• Julia Language

  • Slack (Data, Turing, Appreciation)

  • Julia Forem