An introductory probability course from MIT taught by John Tsitsiklis, covering probabilistic models, conditioning, random variables, limit theorems, and Markov chains. It is offered on edX and mirrors MIT OpenCourseWare's 6.041 material.

Commenters call it amazing, very good, and one of the best courses they've taken, praising its rigor and the time-intensive exercises.

What commenters said (5)

1. Strongly recommendsbdastous2018in “Ask HN: What are the best MOOCs you've taken?”

   I concur on the MIT edX Probability course. I spent at least 15 hours a week on the exercises but it was well worth it.

2. Strongly recommendsharry82018in “Ask HN: What are the best MOOCs you've taken?”

   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

3. Recommendsrandcraw2017in “6.S094: Deep Learning for Self-Driving Cars”

   I recommend Coursera's Coding the Matrix (linear algebra and a bit more) http://codingthematrix.com/ and https://cs.brown.edu/video/channels/coding-matrix-fall-2014/

   And edX's Intro to Probability (MIT 6.041x) https://www.edx.org/course/introduction-probability-science-... BTW, this course starts again on Jan 17.

   I don't know any good MOOC statistics courses. Most that I've seen have oversimplified the concepts into recipes.

4. Recommendsrz2k2015in “Software development skills for data scientists”

   I thought they got better compared to the first few classes, but they do really revolve around R. For a rigorous treatment of the subject matter, the MITx course on Probability is really good. [1] You could also take a look at the two JHU "Mathemtical Biostatistics Bootcamp"[2] courses. Those are also quick compared to the MITx course, but a little more careful about the math than the courses in the data science specialization are.

   I haven't ever used Spark, and I like R, but I am going to take the Berkeley/EdX course.

   [1] https://www.edx.org/course/introduction-probability-science-...

   [2] https://www.coursera.org/course/biostats & https://www.coursera.org/course/biostats2

5. Criticizesmuraiki2014in “Machine-Learning Maestro Michael Jordan on the Delusions of Big Data and Others”

   Thank you for your advice. In my case it's not "re-learn linear algebra" but "learn linear algebra... after first learning calculus and how to understand/write a proof." :) At 32 I'm not certain if this is a worthwhile way for me to go...

   That being said, I haven't given up completely. I'm starting to read "The Haskell Road to Logic, Maths, and Programming" in the hopes of finally being able to grok proofs. At the very least, I feel that learning more math can only help me as a developer.

   For others reading this, this edx course on Probability seemed like it was really good, until my lack of maths background caught up to me: https://www.edx.org/course/mitx/mitx-6-041x-introduction-pro... For Linear Algebra, check out http://www.ulaff.net/

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