#119 overall
Statistical Rethinking
YouTube
4 recommenders · 4 threads · recommended in 3 years, 2018–2023 · score 3.6
A course on statistics built around Bayesian modeling, taught by Richard McElreath alongside his textbook of the same name. It covers building probabilistic models for real-world data, estimating their parameters, and fitting them with R and Stan.
Commenters say it will make you relearn statistics and call the most recent lecture round excellent.
Recommendations by year
20062026
The most recent round of lectures for this course are excellent. You can find them here: https://www.youtube.com/playlist?list=PLDcUM9US4XdNM4Edgs7we...
I'm happy something talking about Statistical Rethinking has finally made it to the front page. I've tried and bounced a few times to raise awareness of this awesome course.
I've found that Statistics is one of those topics that changes your world view about everything. You can consider pretty much any issue statistically, and that will enrich your perspective significantly. In that sense, Statistical Rethinking will help. However, it's a book on Bayesian stats, it's quite dense, and examples are coded in R. It may be overkill for web app metrics interpretation. For that you may be better served with basic stats & inference, frequencies, descriptive statistics, percentiles, basic distributions, data visualization (e.g., trend lines, scatter plots, boxplots, histograms), etc.
To be clear though, Statistical Rethinking is a beautiful piece of work. You can check out the author's lectures[0] and see how much they suit your needs.
[0] https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XH...
I'm a bit disappointed as I didn't see some of my favorites here :D One of them has already been mentioned, but I'll add it anyway.
- Harvard Stat 110: awesome and somewhat challenging lecture series on probability. It goes into all the probability basics, but also goes into problem solving skill very often, so the problem sets tended to be hard as I recall it. But the nice thing is that a lot of it you can find the solutions which are very well written -- and for the exams as well. Also, the lecturer Joe Blitzstein won best professor at Harvard if I'm not mistaken. https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...
- Statistical Rethinking by Richard Malkreath: man this one will make you relearn statistics. And with a heavy bayesian flavor, which if you hadn't had the chance to learn, will bend your mind as well. You will learn to build models that can describe a lot of situations in the real world, and estimate the parameters from data. Cool stuff if you ask me. https://www.youtube.com/watch?v=BYUykHScxj8&list=PLDcUM9US4X...
- Frank Harrel's Bioistatistics for biomedical reasearch: Frank Harrel is the go to guy to understand how to use data in clinical trials and diagnostics research. His book Regression Modeling strategies is a gem that every data scientist should read. This lecture series is aimed at biomedical researches, ie. people without a strong background in theoretical statistics. In the lectures he talks about the best practices and pitfalls you'll come accross when doing and reading research, and also explain some R code to do a better job. Harrel also wrote some very important R packages i.e. Hmisc and rms. https://www.youtube.com/@bbrcourse6203/videos
- calling bullshit in the era of big data: this is a last year course so it is very laid back in the discussions. I didn't go through the whole thing. But what I watched I remember it was really nice and thought provoking. https://www.youtube.com/watch?v=A2OtU5vlR0k&list=PLPnZfvKID1...
- statistical learning by Hastie and Tibshirani: these are the guys that wrote the two main books on statistical learning. If one wants to get into DS, this is the place to start. https://www.youtube.com/watch?v=LvySJGj-88U&list=PLoROMvodv4...
- Discrete Differential Geometry by Keenan Crane: ok, I didn't see the whole thing, because it was above my understanding. But the graphics and images are so eye catching I almost wanted to just sit there watching. I'm pretty sure this and his computer graphics lectures are aso engaging as hell and hidden gems of the internet. https://www.youtube.com/watch?v=mas-PUA3OvA&list=PL9_jI1bdZm...
Currently enjoying the 2017 lectures (and I bought the text) for McElreath's bayesian stats course:
http://xcelab.net/rm/statistical-rethinking/
Strang's MIT OCW Linear Algebra is pretty good. Probably also needs the textbook.
John Tsitsiklis' MITx edx Probability intro course is probably the best course I've taken anywhere and better than anything I did in person at university. I didn't buy the text for this one though I probably should.
Robert Sapolsky's Human Behavioral Biology Stanford lectures are well worth watching. https://www.youtube.com/playlist?list=PL848F2368C90DDC3D