Links don't work. Very little information provided, It confuses more than enlighten.

too fast .... needed to pause the video constantly

Like many others, I give this course a high rating while lodging a minor complaint that there wasn't much instruction provided. The lectures were excellent, if brief; it's hard to imagine anyone having objections to the instructor. But in terms of teaching the material, it was a bit of a drive-by. The lectures show a few examples, while not explaining the syntax or the various parameters. You have to draw that out of web sites and cheat sheets. If you're not adept at doing that, proceed with caution here. In the end, I was worn out from the effort, but felt that I had gained a lot.

The assignments were challenging for me because this was my first hands-on experience with Python, much less with Pandas. I did not find Stack Overflow as helpful as the instructor suggested. Nor did I find much help in the forums, but that's not quite my style.

My bottom line is that the course was time well-spent, but it could easily have been a six-week course with a more deliberate pace through the various pandas mechanisms such as merging and grouping.

FWIW: My recommendation is to get to know Jupyter Notebook early and follow along with the lectures by opening the Week[x] files in the course download folder. You can pause the lecture while you go play with the code to make sure you understand it. Also, I recommend working with a local version of Jupyter and keep your files local. Otherwise, Jupyter loses connection to the kernel, and stops being able to save your work. The messages are disconcerting, and if you've worked yourself into a frenzy, they can cause panic and confusion. So do all the work on your machine and then upload the whole assignment when you are finished. You upload on the "Create a Submission" screen; it takes only a sec. You won't even have to worry about details like file paths; they'll be the same either way. Once you get the hang of Jupyter, you can settle into a work routine. Learn some of the keyboard shortcuts.

Excellent course. I learned a lot about Phyton, even I thought I already knew what Phyton was, but here Phyton is used intensively.

The tests were really tough. I spent hours trying to figure out how to pass the tests. Also, there is a lot of help in the forums, and a lot of people willing to help.

To be an introductory course I struggled a lot, is a very practical course, and the assignements encourage you to learn more. This is the best technical course I have taken. Lo recomiendo ampliamente

Not nearly enough reference content in lectures. It needs to be made clear students coming from the Python for Everybody course (other Umich course) has a book which I was used to referencing for all of my questions (the class was pretty well self contained and did not require much looking up of concepts). I tried to learn this class the same way I did for the previous one and that totally did not work - I spent wayyyy too much time on my first pandas assignment thinking all of the answers were in lecture/notes. The lecture and notes were very very scant and not well explanative about data structures that are very complicated. Please either write a book or make it more clear how students should learn. Yes, the teacher tells us about stackover flow but I didn't know he was implying for us to use those resources. He should say something like "we don't offer a book with this course so use online resources" and not tip toe around the topic because people paid money to learn so take responsibility and make these changes please. I passed but it was very frustrating at first.

It was informative but i felt the assignment part needed more clarification. I faced the problem that even though my solutions were right the autograder gave me lesser marks. I figured out that we must not print to the console. If that was clearly mentioned life would be easier.

Solid course. I definitely would not recommend it to someone who doesn't have advanced beginner to intermediate python knowledge, though - while it does a good job at a review level for the necessary python, it really moves through the code details quite quickly.

The course is very task oriented so most of the learning comes from the assignments solution, not from the lectures. Succeeding in the course demands a lot of time for the assignments and quite often you would need to google " pandas how to...". If you want a self-contained course, this is not a good option. However if you want a realistic approach to data science, it may be a good choice.

It would be nice if Coursera could update the Python environment used for the exercises and assignments to something recent. The version they're using (0.19) is fairly old. Every single assignment that I had running against 0.24 had to be altered in some way to work for 0.19.

Course is very good but the Assesments are VERY time consuming. You have to cleanup the data by your own (e.g. rename football teams by hand, what is really painfull for someone comming not from USA)

The assigments' questions were not always clear, but the real issue were the reports from the automatic checks on the answers one submits: puzzling, sto say the least. The rest of the course is OK.

This course was really challenging, I had to look for information per hours, besides I wanna thank the forum debate. I gave 3 stars because they could improve the teaching techniques.

Good course overall, but more material is required to be learned outside of this class for the required assignments than what is actually taught in the class by a very wide margin

Very limited gaining of knowledge based on course materials, most of the effort is self-learning, internet searching, and lots of readings. Inefficient.

Lots of useful content, and a promising structure. But, the overall level of polish was distractingly low, especially in assignments (unclear & buggy)

I don't think I've learned much along the course. I had to pick a few concepts here and there, but I don't think that the way in which those are explained would stick.

Also, the course seems rushed: I'm not sure what the end game of these courses is, but I think it's an incredible wasted opportunity when it comes to MOOCs, as there could be more lengthy videos and more and better ungraded exercises (something that in this particular course do not exist) and much, much better explained assignments (I guess adding there the info from the forums by the teaching stuff would not hurt).

For being a course of intermediate level, the videos and explanations are too short; there are even places where things are left totally unexplained.

Even if it's supposed (and even encouraged) that the students seek information on their own, the lack of context in some places makes it rather difficult. this is specialy more so with the questions that are interwined in the videos, as normally in order to answer them corretly you have to go out and find the related info (something that totally disrupts watching the videos).

finally, the assignments are a wreckage; some of the questions are incredible difficult to understand, if not out right impossible. The fact that there's a lot of information added to the forums by the etaching stuff, up to the point that the more complicated questions are easily answered with that same infromation, proves this.

I do think there are examples of courses in Coursera: I recently completed "Mathematics for Machine Learning: Linear Algebra" and even thought I don't think it's not without its issues, I find it a much more challenging, entertaining and fun course, that covers in a good way its subject.

I have to commend the people from the teaching stuff that are in the forums, thought, as it's the only course in which I found people from the teaching area activelly participating, and helping the students.

In my opinion not really a course. You get simple hints in the videos what can be done with dataframes and co and 90 % has to be find by yourself in the internet. Very often the teacher says after running a code "as we can see...", but you can see it only if you run the code in parallel, because the teacher does not show the relevant result. For the assignment 3 you have to search a lot in the internet and use trial&error to get the correct result. The notebooks are not very useful for looking up later and you have no slides to download or similar.

The questions in assignment 3 are very sloppy and inaccurate written. Check the forum contributions before starting to avoid a lot of mistakes due to possible misunderstandings.

The speed is very fast (even at 75 %) and all topics are only touched upon briefly. The only advantages compared to Youtube videos are that you have assignments. There should be a lot more exercises in between instead of having after hours of videos only a difficult assignment.

I had done "Python for Everybody" from Charles Severance which I had found excellent, with the instructor being passionate and the pace being just about right. I had assumed it would be similar for "Introduction to Data Science in Python", but that wasn't case. The delivery of the course is at a very very fast pace, you don't even have time to stop and absorb the functions and methods that you are supposed to learn. The instructor and the research assistant will list the functions and methods one after the other without pausing. The assignment is then extremely hard with no resemblance to the material in the course (I couldn't do it even after having reviewed the videos). After holding on for the first 2 weeks (it's a very useful topic after all), I gave up and decided to learn from the "learning the Pandas library book", which is a very good summary of the main Pandas functions and methods (and which was recommended by Dr Christopher Brooks), and I was able to follow it very easily.

This course is not very well structured. A lot of the things that are on the assignments/quizzes are relegated to readings in the books and never discussed in the videos. The book readings are overwhelming for a week worth and require at least 2 times more to finish than what is suggested in the course. That is assuming you want to run the code in the book and not just do a hacky job of just reading it and not understanding the code.

The instructor is fine and does well enough but the structure of this course needs to be reevaluated and the time allotment needs to be made by someone actually doing those readings/assignments and not just an idealized number that they expect unreasonably from their students.

There are better courses to start with your data science journey and this isn't the one to go to, in my opinion.

Really appreciate this course. Got me started in Python, Pandas, and Jupyter. First week felt like magic. I am giving it a low score because the assignment questions were so ambiguous that it required constant resubmits an scouring the forums. The ratio of learning of course content to required Stack Overflow internet research was way off balance.

I learned a lot but was extremely frustrated and burned a lot of time it what I felt was all the wrong places.

Still grateful for this opportunity. I think the questions can be better explained and tightened up.

The assignments are fine, they are pretty tedious at times, but it is this kind of situations that forces me to self taught myself. Something really bad about this course is the lectures. They assume we know everything, I wouldn't be able to follow if i haven't done python in data analysis before, g, so they go fast and doesn't explain how everything/every function works. But if they assume we know everything, there is no need for the lecture videos. Just give us the assignments and just ask us to look at stackoverflow. The videos are 90% useless.

This is not really a course. 2h of lectures in total. I have been in longer one-day university lectures. You have to attend other courses in order to be able to complete the assignments because 90% of what they ask is not in the lectures. This is a compilation of exercises, not a course.

On the other hand, the assignments and exercises are OK, that's why I gave it 2 stars.

the course speed is very highand assuming high level of knoweldg

This is in fact the worst course so far. Mainly because of auto grader. Here are my reasons.

Actually I did not complete the course at all. But I suddenly got a message saying that I have completed the course. I was working on the first problem of the 4th assignment. I did a provisional submission to see if my answer was right. Auto grader reported the grade for the 3rd assignment and said that I have passed the course. Any submission I did after that was not graded at all.

The assignments are not very clear. Looks like I had a older version of the questions while others had a different version. I was stuck in a particular problem because auto grader did not give me a clear feedback as to why I was incorrect. I wasted too much time on this already.

The assignments require too much research outside what is covered in the videos. I don't feel that is right. The assignment requires that we research on Stack Overflow and Pandas documentation. I strongly feel that such activities should be performed only outside the course work when we try to solve real world problems. Course assignments should be reasonably given based only the materials covered in video. This was taking too much time.

T

The discussion forums are not giving clear hints. When we are stuck in a problem, we are not able to proceed further. I still son't know the answers for certain problems because the coordinators do not explain the answers well. When we complete assignments we don't get to see the instructor's solution.

The video instructions were too fast paced. The instructors do not pause and explain critical aspects of the code.

Overall I am very disappointed with this course. There are much better videos on Youtube and Lynda than this . I am sorry. I never thought it would be this bad. The first course on Python from University of Michigan was really very good.