The topics are useful and the videos are good, my one gripe is that in my experience the assignments take much longer to complete than the suggested time. I was completing the training alongside my fulltime job and had to push back my deadlines several times.

I thought this course was very interesting. The assignments were a little more difficult to complete than I expected. I think a few more smaller assignments that highlighted the skills necessary to complete the final week's assignment would have been helpful.

Excellent material to get you quickly started in data-science with python. The course coverage is less, covers just a few topics. The assignment problems, at the end of each week, are of high quality, but the issue detection can be sometimes be daunting task.

Assignments are very good....the background of video's is distracting and explanation of examples just by talk and without proper slides is not so good.

Please adjust the time it takes to finish the assignments to at least twice the posted approximate time.

The materials are very limited here, most the learning is done from outside materials. While on one side it is good because we have to actively researching, it will be better if they provide a bit more of materials here. But the assignments are really good.

Very helpful for learning data science, but the videos go way too fast and don't cover nearly enough material to do the assignments. The assignments do force you to learn how to python, but through lots of googling, stack overflow and pandas documentation.

They follow a practical approach and present you only the very relevant material so you do not stuck too much into theory. Note that this is an intermediate-level course and you need some experience, self-learning skills, and some patience to be successful.

This course will help us to get more knowledge on python & machine learning concepts thouroughly.

Also this cousera certificate gives benfits to us for semester usage and job point of view.

happy to learn so much of new concepts in online at our home

Thanks!!

The exercises and content was very good but instructions/explanations were not very detailed with most of the concepts left for self study. But overall a great course in terms of getting you started with pandas for effective data analytics on tabular data.

The content and the instruction were great: just the right level for someone like me with experience in other programming languages a decent familiarity with stats.

There were a few glitches with the auto-grader for the assignments, but nothing to onerous.

A great intro to pandas. Challenging even for those with experience using the library. There were some struggles with the autogravder, but they were being worked out, and the integration of the Jupyter notebook system directly into the class is fantastic.

While the course was great, the Jupyter notebooks and the background shells gave me several unnecessary errors. While this was a smaller issue, I think the practice Notebooks should be somewhat in line with the assignments. I struggles on Assignment #3.

The course was a unnecessarily hard because of the lack of feedback from the grader, unclear requirements / function definition in the final project and difference in the file downloaded for the project and the one used in the Coursera python notebook.

Good materials and videos, iteresting exercises - the only thing I would improve is the exercises description - not for all of the exerisises it was clear enough for me (especially as I'm not from the US and I'm not familiar with your census data :) )

I learned a lot through the challenging assignments, but the course materials (videos) are not very useful. They only cover the very basics for the assignments, so be prepared to study a lot on your own. Knowing pandas beforehand helps a lot too IMO.

This is an exceptional undertaking by the university of Michigian for benefit of the students in the field of data science and Machine Learning.This is perhaps the only course which focuses on real world application of data science skills to practice

Good explanations, well structured. However, I wish the weekly content would have prepared better for the assignments. If I have to add another 3h per week to find the right advice on stack overflow, that must be stated somewhere so I can plan ahead.

Very good course with a lot of material and challenging assignments. Gave it only 4 stars because in some assignments is more difficult to agree with the autograder than to get the correct answer (e.g., data type mismatch between float and float64).

Excellent explanatory videos and lot is covered in just four week of course but it definitely need good grasp of pandas and numpy libraries for passing the assignnments. I definitely recommend this course as it will push you to learn and do more.

The content is really not good for novices. But the challenges I faced during assessments, did a lot of help. I can now understand the topics much better and at the least I am able to plan to clean the data and work on it much better than earlier.

The course content is great. However, basic knowledge of python programming language is required. Since I didn't know python coding rules, I really struggled for a while. Maybe, basics of python in additional resources will help a lot of learners.

In my view, It is recommended to study a book on Python data analysis tool-kit panda and numpy. The course video quality is very good, instructor voice is clear and load. I highly recommended to take this course who wants to be a Data Scientist.

This course would be recommended for anyone who has got good python skills already and want to do data analysis using python at a quick pace. Overall, It is fun doing the assignments and thinking more in a pandas way rather than in pythonic way.

The projects were extremely helpful to learn various concepts . The videos could have been a little more descriptive to equip us with a little more knowledge so as to tackle the projects with a little ease .apart from that it was really great

Class was good, and the information was presented in a way that was straightforward to understand and apply. I wish the exercise autograder provided more feedback. Sometimes the lack of actionable information made it difficult to solve problems.