HN Academy

Week 1 Linear Algebra https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra...

Week 2 Calculus https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53...

Week 3 Probability https://www.edx.org/course/introduction-probability-science-...

Week 4 Algorithms https://www.coursera.org/courses?languages=en&query=Algorith...

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.

For probability/statistics you could also use the MIT Course https://www.edx.org/course/introduction-probability-science-...

Same course if you prefer the classroom lectures http://ocw.mit.edu/courses/electrical-engineering-and-comput...

Or if you want more rigor you can go through these notes that cover the same material but in a more formal way (via sigma algebras and measure theory) http://ocw.mit.edu/courses/electrical-engineering-and-comput...

I also neglected statistics, it seems there's no avoiding it these days. What cured me was a MOOC from edx/MITx called 6.041x. It literally had me close to tears a couple of times. There was carnage, whining and general malaise. I couldn't imagine a better course for persistent programmers who don't know when to quit.

It's been offered during the spring term for the past two years, so maybe Feb 2016 will see the next run.

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

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

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/

BTW, edx.org is offering what looks like a relatively rigorous intro to probability with calculus. [https://www.edx.org/course/mitx/mitx-6-041x-introduction-pro...]. They say it closely follows this course on OCW [http://ocw.mit.edu/courses/electrical-engineering-and-comput...]

It starts soon, Feb 4th.