Even though I am already a heavy user of Pandas in my daily work, this course forced me to learn several useful features that I had never knew about or bothered to learn. The exercises were challenging enough that it took a decent amount of time and effort to complete them. There were many technical challenges with the autograder and the coursera hosted notebooks that made this more of a challenge than it should have been.

A great starting point for venturing into Data Science, for students/engineers who have some programming background. In my case I had the basics of Python covered , so it wasn't too hard to catch up.However, for enthusiasts with very limited programming experience.... Beware !!! It will appear to be too fast. My final conclusion .... spend 3-4 weeks to learn Python fundamentals and then enroll .... its very enlightening.

The assignments were the best part of the course. The autograder needs work and the problem statements could use some review to make sure all the stylistic requirements of the autograder are spelled out. In some cases, it is well done, e.g. 'your answer should be a number'. In other cases, you have to guess at the order of your answers, since they are expected to come in a particular order, which is not spelled out.

The lectures seem to be giving the bare minimum description of functions and stuff that makes it hard to understand the intuition behind the syntax and working of, say, a line of code that a given lecture tries to teach explaining how it helps serve a purpose. This, in turn, makes it hard to remember the syntax of functions. The assignments are very useful but take a long time since I barely learn from these lectures.

I really liked this course. It gives a good overview of the pandas library and some associated topics. For me, it aligned very nicely with my personal interests. I would have liked some more advanced topics as well, but I understand that this is an introductory course, so it is not in its scope. The integrated Jupyter notebook feature of Coursera is very neat - both for reviewing code from lectures and for assignments.

Pretty good course. I have definitely learned a lot and would like to thank you the lecturer and all the people who were working to create this course. The only comment I have is following. Please, try to formulate the questions more clear in the homework assignments. The assignment # 4 is especially bad in this sense. You can look at the comments of people in the forum to understand that it is not just my own problem.

Good introduction to pandas/numpy. Requires some programming knowledge. Overall I would have liked more guidance during the videos or through course materials, assignments require a lot of self learning (mostly searching through pandas documentation and stack overflow). However the discussion forums are helpful and the assignments are very well designed to guide the student through learning the basics of data science.

The biggest reason for taking the course is it pulls together a few interesting datasets and has a data manipulation project based on the dataset.

The course also pulls together some interesting papers on ethical issues that could confront data scientists, traps data scientists fall into (p-hacking).

However, the material on dataframes covered is too sparse. User should learn dataframes from a pandas book / web sources.

The value in this course comes primarily from the assignments but the instructions tied to these assignments fell a bit short. It would be immensely helpful to have a short FAQ explaining how to set up your environment (i.e. which packages and versions to use) along with test files to verify assignment outputs. Digging through the discussion forum is sufficient but the ambiguity does lead to unnecessary frustration.

IMHO I had to do too much self learning besides this course. I didn't come here to listen to instructions that I should browse stackoverflow and documentation papers -- I am doing that without this course as well. On the other hand it was very clearly undersandable and well said whichever was said during videos. Moreover examples were from real world, which made me work out practices that will come handy later on.

I very much enjoyed this Data Science course!

However, I feel like there needs to be a more interactive environment between the platform and the student. I saw the mini quizzes in the videos a step in the right direction.

Also, I feel like if there were more videos, uses of functions, and providing multiple cases of real data science problems would be excellent.

Thank you for all who helped in making this course!

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.

Overall, the course is really good for those new to python and its data science ecosystem and as always the instructor is expert at what he is teaching. In addition to that, the references provided in the course contains much more information for the interested students. The only missing piece for me is course coverage, I hoped to get more details about pandas and regular expressions from the course itself.

Overall, I was very pleased with this class and how much I learned from and practiced within it. I have some adjustments I would make, mostly to the assignment instructions and time estimates. The assignment themselves are well-designed and useful, they just have extremely clumsy communication attached. I would recommend it, particularly since all my complaints are easily fixed. I took it in Spring of 2021.

Positives: I really enjoyed the course and the exercises were a bit tough but helped me learn a LOT of useful information. It is a good course. I will highly recommend this.

Negatives: Some sections are rushed and is not much help. And the software version used in the course is outdated and hence we had to change the code multiple times as the syntaxes are not accepted in the auto grader. This is a shame.

Although is a very nice course, it would be nice to start on easier programming basics. There are specific things that one's gotta' be inspired to be able to think on. Maybe adding a week before week 1, to introduce on certain syntax and intuition so we can put on practice what we learnt on the kernels since the content of week 1.

It was so delightful and challenging, that I want even more. Thank you.

The rating 4/5 is for the assignments complexity . It requires the learner to work through the logic in pandas and self learn through the errors . This is the new way to learn w/o any spoon feeding . I reduced the rating to 4 and not 5 , beacuse I feel more content could have been given taught by the professors or leave a good piece of advice to the learners. 5/5 for Forum support by teaching staff.

For someone knows basic python only some of the things are explained really fast. However, you can catch up during the assignments. A lot of time for the assignments spend on finding possible solution on the forums. From one point of view it is good - so you can find you own style. Form another, if you work full time and have busy schedule - it will take much longer to finish all the assignments.

The course projects rely on material not yet presented and the automated grading system is very sensitive. I struggled with the assignments because when I had a problem it was difficult to pinpoint the source. Some of the blame is on myself for not using the discussion forums sooner, but this is not my first coursera specialization in data science / programming and I can honestly say I struggled.

course material good, mentors very very helpful and active , as mentioned in one of the posts the expected output of an answer in pictorial form can help students a lot , and the assignments were a little on the tough side , even being from a programming background i had to put a lot of effort to figure out silly mistakes and complete this first course but a nice experience , keep it up coursera.

The course is good and provide great challenges to do a lot of things with Python and Pandas. However, I find that its resource material sometimes lacking complex examples. This may be intentional in order for the students to learn from external resources as well. However, this can also causes difficulties for people who are not already familiar with Pandas before they start following the course.

The course was really good and forced me to intensively check on my weak points which I really much appreciated. The only "weak" point of the course is the automated graduation, but it's just a question about getting accustom to it to better organize ones strategy to pass the certificated. Well done, good job Coursera and University of Michigan. It was really fun, I like it and will continue !!!

The course is a good introduction or overview of the use of Pandas, it's pretty concise so you def need a lot of self-learning beyond the short videos to be proficient with Pandas or complete the assignments easily. I spent more time than expected on the assignments because I'm totally new to Pandas. But the materials are overall pretty good and useful, giving you a guidance of learning Pandas.

This course was very helpful as it gives great experience working with real world data, rather than clean and prepared datasets. However, I can imagine this course would be challenging for someone without a lot of experience. I think the course could also have been a lot better if there was a way to see what about out assignments were wrong or see the actual answer when the question was wrong.

Week 4 lectures could have focused slightly more on hypothesis testing, perhaps delving a bit deeper into the thought process and methodology of coming up with hypotheses, designing an experiment to prove it, executing it, summarising and interpreting the results, etc. Since this is major part of programming assignment in week 4, this could have made the lectures more interesting and relevant.