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Statistical Thinking for Data Science and Analytics

Written 3 years ago
It's very unclear who this course is supposed to be for. It skims shallowly into some topics in the lectures, then dunks you into a long technical pdf that you have to read to answer the "quiz" questions. Luckily it's easy (if you're a native English speaker) to skim the technical articles and guess the answers, so I got a good grade despite only understanding bits and pieces. The assignments remind me of high school busywork.

You are often presented with new equation with one worked example, then quizzed only once on it before moving on to something else. There are no projects or any practical work that unifies the very different topics discussed in the course. Don't expect feedback from admins or assistants or instructors in the discussion questions. The admins do not respond at all to numerous student requests to correct errata in the site, like the correct answer not being available to choose.

The professors will write equations and say "you don't really have to know the equation", or will tell you abstractly about the ideology they used to do an analysis without describing what the analysis is. The result is, you don't learn the intuition, and the ideology doesn't make sense without context.

Pros: some of the lectures, particularly those criticizing classical/Frequentist statistics, are really interesting. But they only hint at something deeper and do not go into depth, and do not guide you toward entry-level external materials that you can pursue on your own.

Overall: it's an interesting broad survey of topics in data analysis, but don't expect to learn how to actually do even simple data analysis. There is essentially no hand-on component of the course. Much of the meat of the course is in a grab-bag of links to external articles which are either very simple or very technical.
My rating
A.b. completed this course, spending 5 hours a week on it and found the course difficulty to be very easy.

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