Ratings and Reviews for Introduction to Data Science in Python

Very good introduction to data science with python (numpy, pandas). Only negative aspect is that the assignment questions are sometimes not unambigious.

Course videos should be more and in detail for each concept.

Excellent!

i like the course content. but the assignments need improvement as i wasted lot of time due to unclear instructions. also if the professor can compile more content into slides that will be great.

Tough, but useful and worthwhile

Wonderful Course this is

Nice one :)

The auto grader could use some work, and it should be a bit more clear to users that this isnt't a magic bullet into data science. It requires alot of work and preferably quite a bit of experience with python. But as a intermediate course with intro to data science I think its great and really reccomend it to people who have dabbled with data science before but never had a good roadmap to actually learning it.

Great material, but the lectures go extremely fast and have no external notes. The last assignment is way out of scope.

Like many others, I give this course a high rating while lodging a minor complaint that there wasn't much instruction provided. The lectures were excellent, if brief; it's hard to imagine anyone having objections to the instructor. But in terms of teaching the material, it was a bit of a drive-by. The lectures show a few examples, while not explaining the syntax or the various parameters. You have to draw that out of web sites and cheat sheets. If you're not adept at doing that, proceed with caution here. In the end, I was worn out from the effort, but felt that I had gained a lot. The assignments were challenging for me because this was my first hands-on experience with Python, much less with Pandas. I did not find Stack Overflow as helpful as the instructor suggested. Nor did I find much help in the forums, but that's not quite my style. My bottom line is that the course was time well-spent, but it could easily have been a six-week course with a more deliberate pace through the various pandas mechanisms such as merging and grouping. FWIW: My recommendation is to get to know Jupyter Notebook early and follow along with the lectures by opening the Week[x] files in the course download folder. You can pause the lecture while you go play with the code to make sure you understand it. Also, I recommend working with a local version of Jupyter and keep your files local. Otherwise, Jupyter loses connection to the kernel, and stops being able to save your work. The messages are disconcerting, and if you've worked yourself into a frenzy, they can cause panic and confusion. So do all the work on your machine and then upload the whole assignment when you are finished. You upload on the "Create a Submission" screen; it takes only a sec. You won't even have to worry about details like file paths; they'll be the same either way. Once you get the hang of Jupyter, you can settle into a work routine. Learn some of the keyboard shortcuts.