It's really good but the assignments are super ambigous and you will have to browse through the forum to get the info what exactly you are supposed to do (not exactly a bad thing, but may be very frustrating), couple of times I just changed something ambigous in my code to the "other option" and passed this way submission - probably tutors would like their students not to do that, but it's a good strategy in this situation. The forum is essential in passing the course, I guess it shouldn't be this way.

The course seems to be hurried up throughout. It is definitely not for first-time data analysis students. It takes a bit of time to get used to the lecture speed and style. Most lectures required multiple viewings to make any sense. Some extra effort is required on the part of the student to learn everything. And the assignments. The assignments are pretty serious and require hours of effort to solve. If you are planning to take up this course, my suggestion would be to expect to put in hard hours.

Very Good content. Quite challenging for someone new to data processing. Great Experience. Thank you Coursera and University of Michigan.

Suggestion: I had to google many stuffs to find some python methods and data processing tricks. These things can be provided as reference material. But It is not a big deal as searching and working out my way to the completion was fun.

Also more exercises can be provided before assignment to make the students a bit more acquainted before attempting assignments.

Great! Even for someone with some python / pandas experience, it was a nice refresher / found new tips / tricks. I actually enjoyed the fact that the assignments didn't hold your hand and forced students to solve problems with the forum and other resources. Look forward to the rest of the courses in this specialisation!

Two aspects you could improve is teaching is (1) Multiindex and (2) Regex. It seemed like you purposely avoided discussing these. These should be a mandatory subjects to learn.

This was a great introduction to the material which gave me enough of a background to start doing small projects myself. I didn't find the videos necessarily that helpful - I mostly learned by reading documentation and stackoverflow responses - but the assignments were well-structured and challenging.

My only problem with this was that the automatic grader took some getting used to, and I think that the first programming assignment should have come with some basic notes about how to handle it.

I learned a lot thanks to the challenging assignments, lectures were a little too fast and confusing at times, but the jupyter notebooks provided are very useful to interact with and learn.

Would have given 5 stars if they update their python version and pandas library version, some new functions I used on my local machine don't work on the coursera jupyter notebook, and it is really annoying to have to go out of my way to change code that works in latest versions but not in older version.

This course helped me get started with data science. It contains a lot of useful methods and functions which prove to be worthy in day to day python programming as well. One must have a good understanding of basic python before enrolling for this course and must study the reference books mentioned to ace the assignments. The only CON of this course is that the assignments are difficult when compared to the tutorials but, as I said above, one must study the references to keep up with it.

I found the exams, especially the final 2, to be very challenging. The estimated exam completion time was 2 hours. I must have averaged 20 hours. The exam difficulty is evinced by the numerous questions posted on the forum. Kudos to staff member Yusuf Ertas for his helpful and timely responses! Much material necessary for the exams was NOT covered in the lectures. Although the lectures were excellent, I feel the majority of my learning took place on outside research for the exams.

A good course to start Data Science. However, it would be better if the videos could be more explanatory. This course has very short videos and assumes that the candidate should do EVERYTHING (in terms of research on external resources or reading elsewhere). The assignments are challenging enough for you to learn the technology pretty well but again, there could have been better, longer and more in-depth explanations to cover the assignments. A lot of time goes in doing your research.

Muy bueno curso para entender sobre los fundamentos del manejo de datos con python. Los videos son de una alta calidad y hay harto material adicional interesante. Una parte importante del trabajo es de exploración en recursos de internet, pero sabemos que eso es parte de la pega de un programador/cientista de datos y el curso te impulsa a realizar esto. Eso si la tarea de la semana 3 fue muy extensa en comparación con las otras semanas y eso me hizo perder la motivación en un minuto.

I found this case to be incredibly useful in helping me understand the data analysis techniques using Python. My suggestion would be that the week 1 and week 2 assignments could be updated to have multiple simple questions around various methods to be applied for selecting, inserting, transforming data frames and series. Also, more assignment questions could be included for numpy. Week 3 and Week 4 examples were identical to real world problems data scientists face when merging data.

The video classes are informative and very clear. The exercises during the classes were thorough and allowed me to apprehend the concepts at a good pace. My only problem were the auto-graded exams. Sometimes the questions are not crystal clear and the auto-grader isn't intelligent enough to discern between a logical mistake and some detail about table formatting. Fortunately these problems are discussed in the forums and usually one can get clues on what they can be from the mentors.

The contents and the method of transcribing as they are taught was really great

I am not sure I liked some of the assignments though, I spent a lot of time trying to find the number the autograder is looking for, not quite missing the coding part but the wording of the questions

It'd be nice to actually have the results that are being sought or something along those lines like in the previous course

Other than that, very good support from staff, they are clear, patient and responsive

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?