Ratings and Reviews for Introduction to Data Science in Python

Although I learned a lot in this course, I found the lectures and assignments to be much too different from each other. I would like to see assignments where you must practice what is learned in the lecture. For myself, I feel I learned 1 set of concepts from the lectures and another set from the assignments by spending time on stack overflow and the pandas documentation. Both are good but the lectures and assignments did not "flow" together as I would have liked.

The instructor was great . The assignments were good and helped me learn a lot.

那个notebook很赞

It is a great, brief introduction to Pandas. I think it is ideal for someone who already knows Python but doesn't have much experience with Pandas. Lectures are well structured with quizzes interspersed. Homework assignments are also well structured and use real data.

Very good course with lots of hands on experience. Jupyter Notebooks are very good learning tool.

This was overall an excellent course, I very much appreciate everyone who has made this happen. However, the very last question of the very last assignment I found to be substantially more difficult than everything else, by a very large degree. Because of that one question I ended up moving my session twice and nearly dropped the course. https://www.coursera.org/learn/python-data-analysis/discussions/weeks/4/threads/1Fkg-ryCEeaIRw7T1E5tHA/replies/vK-NSNNOEeaBeg5U4yHl7A is what finally got me over the hump. The instructions were not very clear to me but the price ratio calculation was the key to success. My guess is that missed it somewhere. Anyway, thanks! I will be moving on to the next course.

I can see a couple of problems in this course. How to access and change elements in a data frame (especially the use of loc and iloc) is not explained very well and I believe there is an error in the slides. The second thing is that I have found more powerful methods in pandas documentation, for example assign() or nlargest() and nsmallest(). This is not really a problem if one of the "hidden" purposes is to push students to actively search the docs for what they would like to do. Work load for the assignments is just fine in my opinion, not too little, not too much. Overall I am satisfied.

While the material is undoubtedly useful, te structure of the assessments is not very helpful. For example, in week 3 we are asked to create data frames from input files: these are presented in descending order of complexity for cleanup. Furthermore, the points for most of the questions are 6.66, despite what are very different demands fore each part. In the final week, the dictionary of state names is counterproductive in answering part one (some state names are region names) and one can either get 100% or one fails the course (as one question is worth 50% of the grade.) Some of the autograder feedback is so terse as to be downright infuriating. Add to that frequent issues with the notebook crashing and much of this coursework was an exercise in unnecessary frustration rather than increasing enlightenment.

Great hands on course!

I learned in this course that pandas is a way deeper rabbit hole than it appears on the surface. However rather than teaching me pandas this course mostly just helped me verify that I was learning pandas. The questions in this class need more scaffolding. I ended up skipping most of the in-video questions because I felt that the work I was investing in getting them correct was not teaching me much. More scaffolding could fix this.