Overall, a good introduction to data science. You need some prior experience with Python and statistics - otherwise the course might be a bit hard. If you have these prerequisites the assignments are not too easy and not too hard. I especially loved the auto-graded jupyter notebook assignments.

What I did not like are the quizzes. These are done using the UM-website and you get your grade for a multiple choice quiz 24 hours later without any feedback (at least I could not find it).

Even when I have experience with python, sometimes I feel explanations of pandas functions where not explained with enough dedicated time. Some questions in the homework have ambiguous parts, difficult to interpret. The autograder has technical problems to grade answers. Some information would be very useful to have in the instructions of the assignments, not located in the forums.

I liked a lot the examples themes, ie. energy and economics. Videos are very easy to comprehend.

Loved the course. Thought it did not teach every technique needed to complete the assignment, it definitely gave directions how to complete it. The forums were very useful to submit assignments. The assignments were quite challenging and interesting. It taught me ways to manipulate data. I started looking data in a different sense now and understand how much information we can mine from a boring looking data. I believe I will be able to apply the knowledge in my job. Thanks much

It was my first online course and I liked the content of this course. It was really easy to grasp the concept of series, dataframes, and their methods and attributes. After completing the course, I have got an idea of how the data is manipulated and organised in order to meet certain requirements. The only thing I do not like about the course is that it does not offer a sufficient number of practice problems related to the topics taught. Otherwise, it was a great experience.

Tough without some prior knowledge of data science in python. It will take you a lot of on your own research, debugging, and looking up techniques, which is fine, but I feel not a great "intro" course. I need a better foundational understanding of pandas and numpy and still barely know why I did what I did, just that the syntax I managed to find works. Both numpy and pandas are quite powerful and its easy to see their usefulness, but I would call this an intermediate course.

A very interesting and useful course for those who want to learn how to clean data in Python.

I loved the opportunity to work directly on the notebook as I was listening to lectures.

I think that more emphasis should have been put in the pros and cons why and how of using the pandas lib. functions in order to accomplish tasks.

Assignments were very instructive and well explained by staff in the Discussion Forum

Many thanks to the staff and to U. of Michigan for the course.

PROS:

1) Course demands students to perform their own research for answers, which is how it is in real-life.

2) Good Assignments, which make you think.

3) Helpful mentors on discussion boards.

CON:

1) Poorly worded assignments, evident by the number of clarification questions posted on the discussion boards. Much time could have been saved if detailed instructions were part of the assignments.

2) Version issues with Python version for autograder versus my local machine.

The course is really based on a hands-on approach and the instructor expects you to navigate though different sources (books, stackoverflow etc) to deepen your knowledge throughout the program. Maybe video contents could be more detailed and focus should be a little less on self-study. Assignments can be quite tough (sometimes too much) but are also helpful in better understanding the subject and how to 'play' with Python programming. Discussion forum very useful.

I found that the videos weren't entirely helpful when tackling the assignments. I had to outsource most, if not all, of my inquiries to external sources (read: stack overflow). Even simple things such as advanced indexing, such as multi-indexing, was unclear. I understand that not everything can be covered, but I feel that basic things should be. Other than that, I enjoyed using the Jupyter notebook, and thought this was integrated very nicely into the coursework.

It was a good experience. Learned a lot of things and assignments really gets your brain to work and explore. But, sometimes, a bit more of explanation would have helped for assignments. Like, for me personally, understanding 'for' and 'if' loop was difficult to get as it was used almost everywhere. For, that i had to see other videos and learn from somewhere else. It will be helpful, if more questions to practice will be given before directly giving assignments.

Introduction to Data Science in Python is good starter course helped me to introduce to lot of concepts needed for basic retrieval, cleaning and manipulating. But I thought teaching can be little more expanded to give more detailed information. Assignments were good, but some are little complicated for the starter. I appreciate Staffing team in helping to us in all possible ways with approach and expected behaviors, validation scripts etc. Thanks to all Staff.

i like the course quite a lot. It is very systemetic and well designed. However, personally i think week four is relatively insufficient. Maybe the explanation of mathematics could be less ( coz those are quite basic math at University) and add more test type and examples. Besides, as for the assignment, I think for some quesitons, the explaination is not clear enough, like in assignment 4 last question, "between start and bottom" includes start point or not?

Quite a fast-paced course, with very quick and packed lectures. Would have preferred the course to be slightly longer e.g. 6 weeks and covering more content. I actually found the style of the coursework to be a plus - though it involved a lot of Googling, this is more reflective of real life, where answers aren't just handed to you on a plate. However, the instructions for the assignments aren't very clear in places, which can easily cause you to become stuck.

This course is worth due to its challenging assignments. If you are taking this with no previous knowledge of data science, you will be expected to make a lot of complementary research.

There is definitely room for improvement in the video lectures - the contents are somewhat limited when compared to the difficulty and what is asked on the assignments. The lecture could and should present more examples/problems and how to solve them in the most efficient way.

Nice course to learn data science. I learn a lot of new thing about data science, different python packages used for DS. I felt like week 3 and week 4 assignment were a bit difficult and video lectures should have been in more details and assignments should be more related to the video lectures.

One should have coding background in python in order to complete the course assignments without too much difficulty.

Anyway I'm glad I took and completed this course.

Feels great, I am introduces to the Data Science in Python. But this was too much of crash course. It needed a lot of homework outside this course - Youtube, Python docs, stackoverflow and some blogs. That work should have been added as part of the course. And those statistics almost went over my head. As I have no foundation in statistics, I may not choose for the next level of course - seeing the difficulty of the statistics it has introduces so far.

This course was illuminating and rich in information. However, I noticed a huge gap between the content covered in lecture videos and the knowledge level needed to complete the assignments, which made the assignments way harder than expected. This might be intentional, in order to prompt students to explore the full functionality of python and take advantage of online documentation. Overall, this was a good course to really get my feet wet with python.

I liked this course, it gives a strong introduction to useful data analysis packages like re, Numpy and Pandas. I think it should be used as an integration to other, more structured courses at a university. I loved especially the regex part, because it is really well-structured and helped me reviewing the regex syntax; while the Pandas introduction is less useful because the Pandas package has updated a lot from the date this course was recorded.

This is a great course for those with some Python background, as the professor clearly states at the first lecture. This was not my case but since we were a team of friends we could solve all the homeworks. If you have not taken an introductory course in Python, neither you have a group of friends to work with, then I suggest you not to take this course. Otherwise, It is a great opportunity to explore advanced Python data analysis techniques.

Overall I thought this course was good quality. The videos and lecture material were informative. My only constructive suggestion is that that code is shown too briefly and moves on to new functions too quickly to absorb. The instructor participation in the forum was essential for being able to complete the assignments. But overall I thought this was effective in getting me to learn some Python independently and I would recommend it.

This is a pretty good course to start immersion in data science. Lectures are very compact and without extra water. Links to books and additional resources are provided. You can get support from teachers in the comments on lectures and assignments.

At the same time, the course requires updating in accordance with the latest version of pandas. During the course, I was faced with the need to rewrite my code to satisfy the old version of pandas.

J'ai appris énormément avec ce cours et je remercie les professeurs et tout le staff ainsi que tous ceux qui ont participé dans le forum et qui étaient d'une aide précieuse.

Cependant je trouve que plusieurs questions sont mal posés et une grand partie de mon temps je l'ai passé à essayer de comprendre ce qui est demandé.

J'espère que les questions seront mieux posés et que l'effort sera consacré à l'apprentissage de nouvelles techniques.

Tough course for a "Introduction to..." course title. Good support resources saved the day. Learnt from doing the assignments and following the tips from the teaching staff. Very practical assignments. Gave a flavor of what data science work involves. Challenging and engaging. The course videos are very well produced. Prof. Brooks is motivating and energizing. The course also gave a human dimension to the work of data science. Thank you.

I enjoyed the course, overall. The assignments were challenging and required some help via stack overflow, but they were do-able and not impossible. The notebooks are great for experimenting and self-learning, but the lectures didn't go into enough depth, for my liking. In addition, the speed of the lectures was too fast on many occasions. I'd recommend having some programming background and/or familiarity with Python before taking this.

The course has indeed taken place over much of what can be considered introductory in data science. However, there is nothing introductory about python, which may bother a more novice student, and the second week of the curos is much more challenging than the others. I believe it is possible to build this same course in 5 weeks to teach more about python and reduce the difficulty of the second week without compromising depth and quality.