The course is designed in a good manner. I would prefer slightly more material in the videos. The exercises are good, they definitely took more time than assigned. A few times question in the assignment is not very clear (that need a bit of work). I definitely learn many new techniques by listening to the videos, changing the class room notebook and last but not the least assignment.

Nice introduction to using Python, but lecture vignettes moved a bit too quickly. Assignments required far more than what was presented in lecture. If not for a particularly heroic mentor, assignments might not have been doable for some like me. On the whole, though, I am grateful for the course and that, at the time I took it, all materials and assignments were available to auditors.

The course contents are well structured but the video lectures are not enough to master the assignments. More detailed explanations could have been included, since its really difficult for a person who is new to PANDAS. The assignments were challenging. Feed back for the assignments submitted were not found so useful. Although, the course contents provide enough space for learning.

For me, this was a difficult course in which I learned a lot. I did not find the materials (videos etc.) provided in the course so helpful, but the assignments you get for your own programming are very close to real world problems and will give you real experience. So you will need some other material to learn, I recommend the book "Python Data Science Handbook" by Jake VanderPlas.

Unlike other courses the lectures are packed with new concepts so much that you can't miss even a minute to understand it. The assignments are fairly challenging. The only part frustrating was working with the grader. Often it won't work and you are left wondering what went wrong without a great explanation. Overall great first course in the series. Looking forward to the next one.

It was a challenging course as a lot of things in the assignments you have to learn yourself. But it was the right way to do as during the work often times one will get stuck on something without any step-by-step directions on how to solve certain issues. The only thing I didn't like about the course is a bit unclear questions in the assignments which took some time to figure out.

Overall, I liked the course but there were some flaws (not too big ones but still worth mentioning) - The way things were explained seemed more like just giving information about something rather than explaining it well (of course at times!). Exercises were really very good as they promoted individual learning more than just depending on what was taught. Recommended for beginners/

This course was tough but I learnt a lot. I really wish that they posted the answers for how the lecturers would approach the questions (even if it was only after you passed the quiz. I walk away from these quizzes not always sure if I made a bad plan that works or if my solutions is good/elegant. This would be very useful especially for us who are early in our coding careers.

I really enjoyed this course, but i am so glad I came prepared and had completed other Data Science tracks on other online sites (Codecademy, Dataquest, DataCamp, etc). I had to put all the skills learned elsewhere to good use in this course. The course was challenging--which is why I wanted to take it! I feel like I had to problem-solve, code, and work with data at a high level.

The material is great, but a tad quick-paced. A little more detail into some sections could be helpful. My biggest grievance with it is that the notebooks and autograder use an extremely outdated version of Pandas and NumPy, which leads to a lot of the official documentation being unhelpful. You can fix the problem within the notebooks but the issue persists with the autograder.

Great professor to listen to and pretty good videos but some assignment questions super challenging and very different from the examples. First three quizzes were weird as they were mostly simple but took 24 hours to output grade. Other than these two complaints, good course to understand how data manipulation works and to learn more about Python's data specific libraries.

This course has greatly helped my understanding of not only some python, but pandas as well. My only suggestion to anyone that wants to take this course is to make sure to allocate as much time as possible to it. They give estimates as to how long it should take you to complete a week's worth of material, but this can vary highly from person to person. Overall, I enjoyed!

Despite having some basic python and pandas skills, I have learned a lot with course. The assignments are challenging and you need to do a lot of self-study, but I find that it is a good way to learn. The only reasons I am not giving a five-star rating is that some of the questions in the assignments (especially week 4) are very unclear, and can definitely be improved.

The knowledge offered in this course is very useful and to the point. However, I found it quite fast , as the students enrolled in this course has no idea of data science so it is quite difficult for us to understand the material at such a pace. Apart from this , the quality of the material was excellent and helpful.

Looking forward to enroll myself in other courses too.

I think Hypothesis testing was not given a lot of time and was barely skimmed through. Another problem was the auto grader itself which was quite painful to work with. I think Coursera should have the latest version of packages on their server as without that there were always inconsistencies with the way my offline python responded vs how the online server responded.

Excellent Intro course. Videos are short and to the point. Does require you to have familiarity with Python and to research on your own to complete the assignments. In that regard, it may be different from other online courses, but I did find the process helpful in gaining a better handle on Pandas data handling. Additional readings are also relevant and helpful.

For a beginner I found this course quite fast paced. One must have a good understanding of pandas and numpy functionality for doing the assignments. The difficulty level of assignments is relatively high to the amount of content being covered in the course itself. I feel more videos should be added to understand the concepts upto the depth of questions being asked.

Very nice introductory course, I think it would have been good to include a brief introduction to visualization. But the statistics concepts and theory were very well explained plus the assignments were designed to apply all of the learned concepts, I would have liked to see more practical exercises done by the instructor as I didn't have a programming background.

Assignments are really tough which will make you do a lot of research and self learning to come up with the desired answers. The pace of the lectures is too quick and hence the depth is lost at times. Will have to do additional research to understand the concepts clearly . Overall, a good course to understand the introductory concepts to the world of Data Science

Value of the course is mostly about personal research and work to complete weekly assignments (whose allocated time is vastly under-estimated). There are some useful examples in the videos but the course would benefit from developing a more explanatory teaching framework to understand and navigate Python. TAs were awesome to support and coach during assignments.

The class material was very good and the assignment improved the understanding.

The only negative is the graders for some of the assignment do not always provide useful feedback. Thankfully posts from Stephanie Greene (a TA) in the discussion Forum provided helpful automated testers that often provided useful clues on the specific answers the grader was expecting

I found the assignments a bit challenging since I was a newcomer to the concept of exploratory data analysis. I would like the background of the lectures to be taken off and get them replaced with a white one in order to avoid any possible distractions. This was one thing I observed with my focus span after having taken multiple courses on Coursera. Thank you.

I really like this approach of the specialization. Data wrangling with pandas is essential. Also referencing some controversial discussions reagarding the limits and problems we are facing as data scientists is appreciated.

However one star less as as there is no feedback on the submissions. So I really don't know what was good and how can I improve my codings.

Great course for data preparation for ML which is sometimes neglected in other courses. Great instructor. Useful assignments. Assignments require research on your own which is good. I would recommend to maybe make the course longer and cover more topics (in-depth string processing etc.), because it felt a bit short. Thanks Dr. Brooks for putting this together.

Bon cours. L'indication de difficulté "intermédiaire" correspond aux prérequis pour réussir les exercices avec Python (ne pas être un total novice). Le plus frustrant est l'auto-grader qui n'est pas assez clair quand aux parties des exercices qu'il faudrait corriger, ou bien qui n'aime pas certains codes pour ariver au même résultat. Hâte de voir la suite !