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Very good content. One downside for me was that being new to Python, Pandas, Numpy, Scipy etc, I found the amount of new information being thrown at me to be a bit overwhelming. Each of these languages/packages could be a separate course even before you start talking about Data Analysis concepts. I was able to complete all the assignments, but I feel like I know "just enough to be dangerous".

Speaking of the assignments, if you're a newbie like me, give yourself plenty of time to complete to work on them. My rule of thumb was to multiply the "estimated time" for each assignment by a factor of 4. The assignment that was supposed to take 2 hours ended up taking my whole Saturday and the 4 hour project at the end of the course pretty much consumed an entire weekend. This might not apply if you have previous experience in this development environment or are just smarter than me ;-)

Not everything that you need to know to do the homework is provided in the lecture, so expect to spend a lot of time in StackOverflow. The discussion forums are also very useful. Sometimes a teaching assistant will offer some hints that make all the difference.

One gripe I have is with the automated grader. It's a great idea, but sometimes you can submit a fairly complicated bit of code and the only feedback you get from the grader is: "Wrong!". My suggestion: have two data sets, one for testing and another for grading. Then students could openly discuss and debug their test results in the discussion forums without violating the Honor Code. They would still have to submit a valid algorithm to pass against the test data.

I am a PhD scientist and heavy user of matlab, R, Stata, bash scripting, and some more esoteric computer languages. I took this course with the idea of covering some background in python skills in a structured manner, the goal being to move many of my data science and some of my data processing code to python.

I found the exercises useful. The lectures are not bad, I just felt they were an overview that either didn't connect much with some of the minutiae of the assignments or they were not always key to me given my background. Eg I found the week 2 videos more interesting; week 4 videos far less so especially the video about running a t-test in python (my statistical skillset is far more advanced).

The real point of frustration is the grader which is extremely sensitive to slight variations. I feel there should be a feedback system where users/students document such cases that could then become a FAQ. Examples:

Grader chokes on type but won't tell me: Submitting string 'True' instead of Boolean True.

Grader chokes on useless (non)significant digits: using round(*,2) at one point crashes the submitted work.

These "errors" are so slight that are almost beyond the human ability to catch them. The result is that, in part, the course turns from 'learning python skills' to 'getting to understand minutiae of what the grader does' which can be really frustrating.

In sum, I believe there is value in this course but the grader is fairly broken and needs a FAQ or similar to warn re choke points generated from trivial differences. I am subtracting stars in the review for that particular reason.