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Statistics with R
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The first 3 courses of the Statistics with R specialization, taught by Dr. Mine.
Skip all the programming exercises in R - just watch the videos and solve the multiple choice problems. Supplement with the decent opensource text book it links to.
Each "week" is likely only 1-2hrs of work. ~5 weeks per course. Only really need the first 2 courses:
. Introduction to Probability and Data . Inferential Statistics
I've taken the first 3 classes in this specialization and would say it's been invaluable. There's an associated textbook available online for free at:
I've taken the first 6 classes in this specialization as well, and have found them to be pretty valuable. These, so far, have been more about the mechanics of programming in R, and less about the math. The Duke one above is more math, less R. But both are an intermingling of both mathematical concepts and R coding. I find that these two tracks complement each other very well.
A set of videos on Statistics from "Professor Leonard". This is just recordings of all the lectures from a standard college Stats 101 class. But the guy is a good lecturer, explains things well, and has a sense of humor which keeps things interesting.
He also has videos on other topics as well, if you're interested.
I believe Kahn Academy also has a section on Statistics and Probability.
You might also find some of the stuff linked here useful:
I also completed this specialization, but unpaid so did not do the capstone project. I agree with most of your take/skip assessments. The Statistical Inference course was particularly disappointing. I recommend the excellent Data Analysis and Statistical Inference (https://www.coursera.org/course/statistics) to fill in that missing area of learning.
⬐ mindcrimeI was just about to sign up for the Statistical Inference class, and happened to read some reviews before hand. I'm glad I did. A number of people have pointed out how that class diverges from the others, becomes very math heavy without covering basic pre-requisites, etc. Everybody is saying to make sure you've taken a least a basic Statistics class before taking that one, and since I haven't had a Stats 101 class, I'm going through a bunch of Stats material before signing up for this class.
I'm still going to take it since I want to finish the specialization, but I'm glad I didn't wind up diving into it blind. I think I would have struggled with it if I had.
+1 for The Analytics Edge.
Along the same lines but a more thorough treatment of linear regression and statistical inference is the excellent Data Analysis and Statistical Inference https://www.coursera.org/course/statistics
For statistics I really liked Data Analysis and Statistical Inference by Mine Çetinkaya-Rundel. I think I understood statistics beyond formulas after I took this course. Apart from typical pen and paper problems, you also get programming exercises in R.
⬐ geomark+1 I recently completed this course. It's really quite good.
I've completed a bunch of Coursera courses. Quality really varies. Even within the 9 course Data Science specialization  track some courses were rather poor while the rest were very good. I'm currently taking the #5 rated course . It is excellent. But I'm only taking it because the Statisical Inference course in the Data Science specialization was so weak.
I would also recommend the Cryptography 1 course by Dan Boneh on Coursera . Excellent if you are at all interested in the subject.
I always download the lecture videos, slides, quizzes, labs and exams because, as mentioned, many of the courses don't allow access once the class is completed.
You definitely have to have plenty of self discipline to complete MOOCs. And I don't have any delusions about a Coursera certificate being useful in landing a job; that's not what I'm after. I'm building the skills I want to apply to my own projects.
⬐ sawwithttps://www.coursetalk.com has quite good reviews, especially on the more popular courses.
They demonstrate using R but its not required.
⬐ rebootthesystemI'll look it up, thanks.