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Probability - The Science of Uncertainty and Data

edX · Massachusetts Institute of Technology · 3 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention edX's "Probability - The Science of Uncertainty and Data" from Massachusetts Institute of Technology.
Course Description

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference -- Part of the MITx MicroMasters program in Statistics and Data Science.

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This course is offered by Massachusetts Institute of Technology on the edX platform.
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Shoutout for another great online MOOC : (It is the same as MIT OCW's 6.0.41)

I did it as preparation for my Masters and it was genuinely helpful. Would recommend it to everyone looking to do a prob 201 before taking advanced-ish courses.

I would recommend Harvard Stat 110 over MIT's probability courses - (you can find full lectures on youtube and the book online)
How come?
Blitzstein is a good lecturer. I think I did this course back in the day.
do you the link to youtube. Cant' find it; that why i gave up on the class.
Youtube playlist -
I found the EdX course too rushed (assuming you don't pay for the verification to get lifetime access). I like Bliztstein, so I instead used his Youtube playlist [0]. It has his full lectures. Also, his book is free to view [1].



The MIT on edX courses for Probability [0], Single Variable Calculus [1], and differential equations [2] are of the absolute highest quality.

In general, I'd if there's a course by MIT on edX for the topic you wish to learn, I'd check it out.




Thanks! Looking for a free version of [2], do you know where else one could find it?
> This is NOT a course for absolute beginners

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, 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.

For those who might speed through 18.650, the natural next step is [0] 18.655 (Mathematical Statistics) followed by the new course [1] 9.521 (Non-Asymptotic Perspectives in Statistics).



I actually agree with your point on prerequisites. I think by absolute beginner I meant to say no experience with probability theory either. With the prerequisites you outlined, I think a determined person could do well with the material. Mainly posting this follow-up to correct myself so people curious on whether they'd be able to follow the course are better-informed.
"I disagree with that, as long as you have some mathematics background (calculus, a bit of linear algebra), and an understanding of probability theory"

right, so it's not for beginners.

What would you recommend for someone who doesn't have the calculus and linear algebra?
Gilbert Strang's linalg lectures on MIT OCW are amazing.
I took a look at the material (the slides on Method of Moments, in particular) and my feeling is that it is a particularly mathematically-heavy treatment of statistics. As it states in its goals, it aims to introduce the mathematical theory of statistics. On the course's main page, it is listed as a senior undergraduate/graduate course. The style tends toward being expository rather pedagogical -- it's a very French/European approach to teaching mathematics.

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.

"Basic" is ill-defined, but I think sloonz is right about it only requiring calc + linalg, which most CS / Eng majors will have taken.
So I've taught undergrad courses, and my sense is, the average US college engineering sophomore with linalg and say Calculus II (but no Real Analysis) might struggle with this material somewhat. They may know the material for linalg and calculus (and may have gotten As), but my feeling is that many would not have reached the mathematical maturity to truly internalize concepts.

I would place this course maybe at the senior level (with graduate level cross-registration)...400-500 level elective.

What is your sense?


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.

Functional analysis in Year 2.

Then again the top French schools filter out non-math folks via classes prépas and exams.

I'm currently doing MITx's Fundamentals of Statistics MOOC, which seems fairly similar to this one. The course material doesn't require too much understanding of real analysis, though the instructor does make cursory acknowledgements of the "technical details" he's glossing over, presumably for the more advanced students. The only analysis concepts we've used are continuity, differentiability, and convergence. It isn't a proof-based course: the instructor does prove some theorems in class, but the psets are all just computation. I do agree that it'll be harder to internalize some of the concepts w/o a background in analysis, but I think you can get a reasonable amount out of the course regardless.

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.

The "undergrad Year 1 at École Polytechnique" is really the junior year, since the freshman/sophomore years of university education would have be done in prépas. It is undergrad, and it would be their first year at the school, but it is quite misleading to call it "undergrad Year 1". Given that undergrad is three years in France, "undergrad Year 1 at École Polytechnique" means "last year of undergrad".
Ah what you say is true... upon examination, this curriculum is for the 4-year ingénieur polytechnicien program, which culminates in a Master's degree (diplôme d'ingénieur).
Note that this is unusual in that it lasts four years, not three.

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.

I'm in the demographic you describe, yet I've had a hard time finding resources to develop that "mathematical maturity" short of going back to graduate school. Which I'd love to do, but am at a point in my career where that would be devastatingly expensive to my future since these are the prime earning and wealth building years.

I wish there was more of a self directed way to achieve this.

Just curious, the topic above aside: what would you say is the main reason behind why you'd like to strive for "mathematical maturity"?

(from what you wrote, I gather it's not for reasons of vocation?)

I just finished an MS in math and statistics a long time after doing a non-mathematical undergrad degree. I feel a lot of what is called mathematical maturity is actually getting comfortable doing proofs, which I think is hard to learn while also learning more advanced math. I would recommend working through "Mathematical proofs" by Polimeni/Chartrand/Zhang. Unlike math at an earlier level, you can't just check your answers against the official ones to see if you made a mistake - writing proofs is more like writing essays, the grammar is the easy bit, it's the process of putting the arguments together in the right detail and the right order that's important and hard to do without feedback. So you also need to get feedback from mathematicians on your proofs if possible. The best way to do that if you don't have a buddy who happens to be a mathematician is to learn to use LaTeX and ask questions on

An alternative is to do the proof and abstract algebra courses via (asymmetric) distance learning at

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!

Thank you for the resources, it is greatly appreciated!
Since it's seems like you were already motivated and interested in learning Math on your own, how would you describe what your learnings were before you enrolled in formal studies? In other words if you could travel back in time to talk to yourself before you made the decision to enroll in a Master's program, what would the younger you have asked the older you and what would be the response? For example I'm thinking a reply might be like "well you're going to miss out on opportunities xyz by commiting to a Master's program, but because I know you and know you wouldn't be happy without satisfying your desire to learn Math in a more formal study the trade of is worth it. And you don't know this yet but when you start learning about P,Q,R you'll really get a kick out of it" :-)
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