MIT OCW: Statistics for Applications (2016)
ocw.mit.edu
ocw.mit.edu
I disagree with that, as long as you have some mathematics background (calculus, a bit of linear algebra), and an understanding of probability theory (which can be taken from the prerequisite course https://www.edx.org/course/probability-the-science-of-uncert...), this course is self-sufficient and does not need prior knowledge of the subject.
I was a complete beginner in the subject and I am able to follow the course without too much difficulties.
It does seem to require mathematical maturity beyond the basics, and in my opinion this is likely not accessible to most beginners without some advanced mathematical training.
If you find it accessible as a beginner then I congratulate you on your mathematical prowess.
I would place this course maybe at the senior level (with graduate level cross-registration)...400-500 level elective.
What is your sense?
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Side note: it's interesting in that in other countries, e.g. say France, the math curriculum is so darned advanced. In undergrad Year 1 at École Polytechnique, real analysis and variational methods are already covered in common courses.
https://programmes.polytechnique.edu/en/ingenieur-polytechni...
Functional analysis in Year 2.
https://programmes.polytechnique.edu/en/ingenieur-polytechni...
Then again the top French schools filter out non-math folks via classes prépas and exams.
I wish there was more of a self directed way to achieve this.
An alternative is to do the proof and abstract algebra courses via (asymmetric) distance learning at https://westcottcourses.com/courses.html
I know someone who took these courses and felt like they got good feedback on their homeworks from the profs running the course.
Feel free to get in touch if you want to chat, I spent a long time trying to self-learn this stuff before starting my math MS, so happy to help in any way I can!
(from what you wrote, I gather it's not for reasons of vocation?)
That said, I did study analysis (but not measure theory) in college, and I don't entirely remember what I learned from calc vs analysis classes, so I may be a bit off here.
Standard curriculum is three years of bachelor's, then two years of master's.
In the prépa - engineering school track, it is two years of prépa, then three years of engineering school that gives out a master's in engineering.
Thus the first year of engineering school maps to a (third) last year of undergrad, the second to a first year of a master's and the last to the last year of a master's.
[0] https://ocw.mit.edu/courses/mathematics/18-655-mathematical-...
right, so it's not for beginners.
I completed my bachelors in Electrical Engineering 14 years ago. I'd been looking to get some formal education in Statistics as I work as a data analyst. The course has been really good and I'm learning a lot of the theoretical aspects of Data analysis that I otherwise would never have learnt thought DataCamp or Udemy or others. I'm glad to have started it, however, it's not practical and it won't help you get a job immediately. It's very much academic in nature, however you can learn the practical stuff from other sources if you need to or learn it on the job. Depends on what you're after. I'm not doing it to get a job, rather doing it to get into academia. Hope that helps.
https://projects.iq.harvard.edu/stat110/youtube
Professor Blitzstein summarizes top 10 ideas on Quora:
https://www.quora.com/What-are-the-top-10-big-ideas-in-Stati...
Cheatsheet for the class: http://www.wzchen.com/probability-cheatsheet
Book is also online: https://drive.google.com/file/d/1VmkAAGOYCTORq1wxSQqy255qLJj...
I like the way that I was taught when I took biostats in graduate school. We fist covered t-test and ANOVA as a way to intuitively compare differences among groups. From that, we then generalized our approach, and learned that linear regression is a generalized form, which happens to have a closed form solution. From linear regression, we then learned about logistic regression and odds ratios, and about the log-link formulation. But drats! We don't have a closed form solution to calculating our Betas. But we can estimate a good set of them using likelihood!
Statistics is often fraught with nuance, and I think the more we can convince to do more thinking about what we're trying to accomplish with a statistical analysis and how to prove it should have more emphasis over just "lets just make an alphabet soup out of our slide decks!". That's not to say you shouldn't have mathematical rigor, but mathematical rigor without an intuitive understanding is potentially giving the impression that more complex analysis == better analysis.
That said, ESL is a better companion than Wasserman if you want to apply the statistics to ML and don't plan on studying the graduate-level statistics courses. ESL + 18.650 + 9.520 (Statistical Learning Theory, Poggio and Sasha Raklin) covers 95% of the math and statistics I've seen in ML research.