Ratings and Reviews for Introduction to Data Science in Python

Although the tests can be a bit fiddly, this is a great course if you already have a bit of background with Python and/or data cleaning. Lecture and tutorial videos are lean and information-dense.

This course was excellent. This course deviates from many garbage MOOC who only work with quiz and can not provide a real python coding challenge experience. Assignements are really tough. But my sense of progress is real.(I have struggled to identify such feel in many pytyhon MOOC). Jupyter base for everythjng is a fantatsic format(it even allows coding mobility betwwen my station at work and my home station through the coding on jupyter in the cloud) . My feedback nevertheless will point to some aspect in my experience and where I think you can improve. Succeeding the assignement does not mean that we identified the most elegant way to apply all the knowledge of the course(lambdas,list comprehension, grouping..., apply) in our coding. Breaking that barrier is not easy for me unless we are forced at it and so my looping mind is often applied in assignments. A real correction with the answer need to be provided(this is what the real classroom would do, we managed to get to the answer but we could still learn more with an assisted correction just like what the real classroom would do.I understand that you are worry that the model will end up as copy paste on a webpage and will kill your value. You could maybe consider this add_on for paid customers only and only provide it in picture way which can only be paper print and not so easily converted to webpage format.Or you need to find an alegant way to randomize the assignment coding test at each coursera session, which in that case would not bring any forgery issue and you could provide the correction at the end of the course(or after each assigment completed). Videos are a bit too fast on concepts sometimes. You could split the assigment in two formats: format where simple principle of the course are first resolved on jupyter notebook (just like the videos case but with more exercices) and complex dataframe case as second assignment .(but please reduce the amounts of case to only 1 or 2, not 3) You could reduce dataframe case.(I've spend easily 40 hours on assigment here, assigment time is too heavy from my workload as a full time scientist. This needs some carefull tuning. Overall Great Job

Really awesome course. It is a kind of do it yourself course.The assignments are tough to crack. I think a little bit of programming experience is neccessary. The lectures themselves only give the basic knowledge. The assignments make you do research on the relevant topics. The autograder is pretty bad, it gives false negatives a lot of times, but that i believe is Coursera's drawback and they are working on it.

Very challenging course. And I thought I knew Python. I learned much, much more about Pandas, idioms, lambdas, regex, etc. I will be able to be more productive at work with all this. Thanks.

Excellent course, I learned a lot especially through the assignments. Missing a fifth star because of a lot of issues with the notebooks autograding, and sometimes imprecise instructions in the assignments. Still I look forward to working on the next pandas courses!

Contents are great and very relative. Exam is fair and reasonable. However students have to deal with an autograder for the scores and the autograder is not up to par for this course. The amount of time that you spend on learning during the course is only a fraction of time you spend to get through the autograder.

The course was challenging, and although I would have liked a bit more information from the video lectures, and a bit more practice on using the basic functions, I learned a lot. I thought the video lectures were a little sparse. There were only 20-30 minutes worth of video lectures per week and the assignments required you to stretch the knowledge that you gained from the lecture videos quite a bit. More examples in video lectures would have helped. I would have also liked to see a few simple problems in each assignments just to get comfortable using the various functions introduced in the lecture. So overall, the course was challenging and definitely a fair amount of work coming from someone new to python (but moderately experienced in C programming), and although I would have liked a bit more guidance, I learned a lot.

Pace of lectures is rather fast and doesn't fit to homework level (which is much harder). But overall course is OK

A good introduction to python and data science. The questions were just about the right level of difficulty. My main criticism is that the online videos were pretty short and not going into a lot of detail, whereas with the questions you had to do a lot of extra research to figure out how to solve them. More interaction with the enrolled students during the course and having more in-depth videos would make the course a lot better.

Very nice!