[note: this is now a Wiki post, so please add resources at the end of it. Thank you!]
All that talk in the first lessons of part 2 on the importance of stats is painfully frustrating for me.
Any brilliant suggestions at how to really get all those VAR/Co-VAR/etc. concepts?
Math has been always easy for me, including pretty advanced university-level math, but no matter what I try, my brain just doesn’t get stats I couldn’t get it 25 years ago when I studied it at uni, and I still don’t get it when I try hard now. It’s so frustrating. I can do problems and follow formulas, but I just can’t wrap my head around anything stochastic. It’s so weird. I can do very complex integrals and derivations, and what not, I’m very good at spacial things, but not stats. It’s like I’m lacking some science genes (I also hated electromagnetic fields and relativity theory, but loved the rest of my courses).
A year ago I took 3 months to follow math lectures from Stanford, Harvard and MIT to refresh what I studied 25 years earlier but have never used it since then. I absolutely loved the MIT calculus courses. I cringed through stats. Tried Khan’s academy stats - no luck either. I somewhat get it when I study it but it doesn’t sink in and a few days later it’s as if I have never studied it.
I have just went to my local library and picked up a bunch of books on stats, thinking to just re-read the parts I need again and again until I make a break-through. I really really need to find a way to understand the basics of that field, since ML foundation is all about stats.
Have you been in the same situation and found a way to make a break-through?
If you get inspired to follow up please don’t just list books or courses, I have a ton of them, and watched and read a ton of them to no avail. I just can’t find any that help me to really make a break through and to really start grokking stats. It’s like I’m looking for that brilliant teacher who has a way to teach stats to someone who just doesn’t get it. And none of the famous professors at the big unis I tried did it for me (unlike with other branches of math - some amazing profs out there).
My academic background is B.Sc in EE (but from many years ago) if it helps to set the stage.
And thank you!
Here is a summary of what resonated for me in the kind suggestions below (meaning there are more suggestions in the answers that aren’t listed here):
You have to be sure to understand everything you’re doing. Go back to the basics every time you’re not sure about something:
Stats is not about pattern matching and requires a different skill/mindset.
But perhaps by doing enough experiments with data it could be reduced back to pattern matching by honing the intuition. Practice with actual data a lot - hands-on approach
- Study systems:
Resources Summary [Wiki]
These were multiple courses and books mentioned, so I turned this post into a Wiki and you can now edit the resource section yourself.
- CS109: Probability for Computer Scientists - start very low-level and build up nicely from there. They have the slides and some nice summary handouts on their website.
- Stanford - Statistical Learning (Self-Paced)
- Richard McElreath’s course, Statistical Rethinking (a popular intro to Bayesian statistics) - He’s an entertaining lecturer and an all-around kind/charming person, plus his course shows a glimpse of a different world from deep learning. it’s also largely programming/simulation-based, rather than math-based
- MITx Introduction to Probability - The Science of Uncertainty A in-depth, extremely well-done MIT-level introduction to probability and statistics. It’s a lot of hard work, but it pays off!
- Peter Bruce - Practical Statistics for Data Scientists - 50 Essential Concepts (uses R)
- E. T. Jaynes - Probability theory: the logic of science (expect to experience a mix of fun and shock feelings…)
- Allen B. Downey - Think Stats - an introduction to Probability and Statistics for Python programmers
- Carlos Fernandez-Granda - Probability and Statistics for Data Science
- Data Analysis a Bayesian Tutorial, 2nd edition, by Devinder Sivia Well written, accessible introduction to statistics and Bayesian analysis.
- OpenIntro Statistics, 3rd Edition, by David M Diez, Christopher Barr, Mine Çetinkaya-Rundel It’s free on the open intro website. They teach foundational statistics mostly from a frequentist perspective and give numerous examples, only trick is you have to convert their library(openintro) to a python package as well as the code in python.