Really good introduction to Data Science. Great lectures and really good exercises to enforce what you have learnt.

If you are wondering to do this course, and have reservations given the many reviews about the lack of spoon feeding, I would advise to still consider it. For some other courses, which have those kind of reviews, have often been spot on.

However, personally, I found the balance to be good in this course. Learning to solve the problems yourself, with the safety net of a great discussion board and grader to tell you if you are right or wrong, you will get there. The approach really allows personal development for problems without either of those two aids. Some have been disparaging about the over dependence on Stackover flow to help finish the exercises. I can see the frustration, but the Fundamentals were taught well, and learning to bridge the gap efficiently using Stack overflow is a large learning.

This was a good course. The professor (Chris Brooks) was EXCELLENT, although much of the material was presented very quickly and was difficult to follow at times. The real learning came during the exercises, which I generally found to be very difficult but also very good at ensuring the material presented in lecture was actually reinforced through hands-on programming. I would recommend making more exercises, perhaps two per week, rather than one very detailed exercise at the end. The last week's exercise, in particular, was very difficult for me and I would recommend breaking part 5 (of 6) of that exercise into several smaller chunks that could be completed and graded individually. Rather than 10-10-10-10-10-50 as the points awarded in that exercise, I would also recommend changing the scoring to 10-10-10-10-35-25 as the next-to-last part of the exercise was by far the most difficult and time consuming.

First off - Kudos to Yusuf Ertas for all his help and patience getting me through the programming assignments. I really appreciate his willingness to help!

I believe a different text book would be helpful. I understand it is optional, however Wes McKinney has created a great reference manual, however he uses too many theoretical examples. I really need real-world, applied examples. He seems to get to into corner cases and shows you 10 different ways to do something, however no clear direction for a new students.

I would also like to see the lectures topics more closely reflected in the programming assignments. Maybe additional assignements after each lecture. The student should then be able to pull from the lecture assignements to complete the final programming assignment.

Need more direction and guidance on the programming assignments for an Intro course.

This course is kind of difficult for those who have zero or little computer programming background. But if you have completed the Python for Everybody in Coursera, you can quickly get on it. To take this course, make sure you have basic knowledge in Python, or you may get frustrated quickly. Also, you can refer to Stack Overflow, Github for solutions if you have struggled for a long time on assignments. But, don't just copy the answers, go search the Python/Pandas/Numpy Docs for further explanation, and try your own way to optimize the solutions.

All in all, this is a very good course to deeply explore data science by Python. One problem for me, an international student, is what the instructor says sometimes confused me because I can't understand the abstract concept just by listening. I hope more graphic explanation could be added in lectures.

'Introduction' is a bit of a misnomer for the course. If you already have even a fairly elementary understanding of analytics/data science, this course adds nothing of value; if you don't have that understanding, you don't really a great intro into what it is. If you already have at least some programming and/or python experience, this course does a good job of showing you the kind of tools Python has specifically for dealing with data; if you don't have any programming experience (especially in python), you may be way out of your depth. The exercises were well designed and were great learning opportunities, but were far more difficult than course materials implied, especially for python beginners. In spite of limited lectured information, the activities helped me get a great appreciation of python, which is the course's saving grace.

In conjugation with learning from the Python for Data Science book this course is a nice introduction to the topic. I would say there is too much scope to pass the course with bad code and not learn much. It will take self discipline to not accept whatever works and learn the general aspects taught. There is a large degree of independent learning required which means that to get the best out of the course requires dedicated time exploring. If I didn't have the book I may have felt lost. The discussion forum contained key information a rushed student may not have picked up, however it is a testament to the mentors that this became readily available along with their support. This course could have done with some peer review to allow code comparisons to check quality, methodology and readability without breaking the honor code.

Generally very interesting and helpful course. Lectures could have been a bit meatier (I spent a lot more reading through the docs than most other Coursera programming classes to complete my homework). It's one of the few Data Science sequences that seem to be offered in Python, rather than R, which was a big motivator for taking this particular class (the transition to working with big data with for example pyspark should be significantly easier).

As an side: one thing that would be helpful is if learners could see the solutions after submitting/getting a grade. Learners would stand to learn a lot by seeing the 'better'/'accepted' answers -- even though I aced the assignments, I _know_ my code isn't as "pandorable" as it could be. That remains one of (a few) bigs differences between a MOOC and in-person teaching...

This course is fairly difficult in regards to the assignments and the lectures cover a lot of material!

But trust me when I say the that the lectures are sufficient to point you in the right direction for the assignments. For the most part I ended up using stack overflow when I was working on the assignments, this is nothing new since most of my past programming assignments consisted of me scrounging through stack overflow posts. This course did in fact teach me alot concerning regex and how to apply it on a pandas dataframe. I learned a lot when it comes to data cleaning and for that I think this course is well worth it! The material and how it's presented does add to the difficulty, but honestly it's a fun course if you sufficiently go over the lectures and use stack overflow for general aid.

The course is excellent. I really enjoyed this course. But I think that the assignments are a bit too tough because sometimes the concepts required in assignments are not covered in the lectures and help is needed from 'Discussion Forums'. Since I approached discussion forums only when I could not think of anything else, sometimes it was very frustrating for me because I got stuck on a couple of problems for even some days. Thus, assignments should be only on the concepts covered or it would be better if lectures could be made more exhaustive so that we can learn more in the lectures itself. Otherwise, the course is great and the person who helps us on the Discussion Forums is super. His efforts are commendable and he is very knowledgable, cooperative and active.

The course material was good, but don't expect to have your hand held during this experience. While the lectures are informative, the assignments often leap ahead in complexity. Each assignment does say that you will have to do some research on your own, to be fair, but the amount of self teaching required is, in my opinion, too much. That said, the problems posed by the assignments do build on each other (even if they don't line up well with the lecture material) and if you honestly take the time to learn the things you have to understand in order to complete each assignment, you will walk away with some new skills and a sense of accomplishment. 4 out of 5 for teaching me some new things, minus the one star for being more of a challenge than I was expecting.

It was hard and I could not have passed nor learned much except frustration if it had not bee for Sophie Green (TA) superb support.

I could not start the course on time but the first week was easy. So I was surprised by the work excepctation from the 2nd week. Also it did not match what was forcasted by instructors. 2h --> 10h, 4h--> 20h... and I'm not count the night thinking of ways to solve the problem.

I think that difficult comes from the expectation that documentation is understandable by newbies.

Also question were often tested on type but the expected type output was not mentioned in the questions.

Finaly, I think personally would need to learn how to debug properly a python program (going step by step in it)

Hard and challenging. Thank you Sophie

This is a good introduction to Python and especially pandas for handling data. However, the course material is not very comprehensive, and you are expected to read online documentation and search StackOverflow to find answers to most of the required functionalities if you wish to finish the assignments. But, and here comes the but, this is actually how you would proceed the day you are faced with a "real" data science task, so from that perspective it is a good lesson. Also, for learners out there not wishing to pay for similar material, there are plenty of notebooks (on e.g. GitHub) that more or less contain the same (for free!). In summary, mr. Brooks does a good job explaining the material and the assignments are hands-on and well thought through.

Good introduction to Python, with a heavy focus on Pandas. Definitely worth doing if you're struggling a bit with the Pandas documentation. The course assessment were a bit of an up-hill battle for me, but I feel more skilled for completing them, so I would encourage others to fully engage as much as possible. Same with the readings that are set. In fact, I'd like to see more recommended readings, along the lines of David Donoho's paper. The course uses Jupyter notebooks for assessments, which was refreshing, and has in-video code to work-through which was also much appreciated. All-in-all, take the course if you're interested in Python and Pandas. It will eat your time quite a bit to do the assessments if you're like me, so be prepared for that.

This course was great to get a start in Data Science. I thought the lectures and the course notes were very well presented and served as a good resource for the assignments. The use of Jupyter notebooks was especially helpful. I found the course forums also very helpful in debugging my code. I have some previous experience with Pythons so the level of the assignments was easy enough that I didn't feel overwhelmed, but challenging enough for me do a lot of independent study.

A criticism I have is that the Jupyter notebooks kept logging me off so saving assignments regularly failed and I had to redo work often.

I usually don't pay for courses on Coursera, but the quality of this was so good that I will definitely continue with the specialization.

The course is overall very good and is definitely a very complete introduction to the topic.

Sometimes I found the pace of the course a bit too fast: it's quite difficult to focus on the Professor's explanations and, at the very same time, follow the commands typed on the jupyter notebook (not to mention reproducing them on my own!!). The python exercises are overall quite well done, even though in some parts they could benefit from clearer explanations (thankfully almost always provided in the discussion section). Be careful that often the programming assignments go beyond what has been explained in the lectures and therefore require an active search in the documentation, on online forum such as stack overflow or in the resources section.

The video-based training for the course was good. I think there is often too big a jump from what is covered in the videos to what the learner is asked to work through in an assignment. Perhaps more, and more modular assignments would help. It would be impossible (at least in my opinion) to pass the course without the teaching assistants and the interactions in the forums. (A big hand for the TA's!) The additional readings and thought-provoking questions are very good. Please continue them. Finally, whatever you can do to get Coursera to improve the (incredibly cryptic) automated grader functionality would be appreciated: there were times when I felt that the only way to get an answer right was to sacrifice a goat at the grader's altar.

This course gave me a workout! I had taken previously taken the first two units of Py4e and I should have taken more introductory courses before this one. Videos introduce a lot of material but go too fast and sometimes are hard to follow. Most learning occurs using other resources - Help manuals, StackOverflow, web videos (I found Data School easy to understand), etc. A lot of self-directed learning that will help me progress and learn independently in the future. The tutors were very helpful most of the time - sometimes responses weren't sufficient for my limited background & I needed followup questions. Overall, very satisfied and feel like I have achieved a good introduction into pandas, but still lots of learning needed.

I would say this course is kind of a crash course on pandas. If you are looking to learn pandas from basics and want to explore each possibility of different methods and attributes of pandas object and classes, this is not for you. But, if you already know a bit of pandas and want to build on that, I definitely recommend this course. The course instructor speaks very fast and sometimes it's kinda hard to follow along with video and notebook exercise. The assignments are a bit challenging and requires double the time mentioned in the course if you are not fluent with pandas. I would look to see some built in interactive practice tool within the course rather than separate notebook files. I wish you good luck with the course.

This was more challenging than expected, worth it though. Had to self learn a lot. Previously had some exposure to the basics of Data Science and still found it hard. I would recommend this if you are willing to put some time into it. I would suggest the course instructors to add more lectures and provide more specifics and nuances/methods in lectures. I would also recommend interested learners to enroll in UCSanDiego's Data Science Program. I felt they had more comprehensive lectures. Though at times there were some quirks that I only learned from the current course. Also just a tip, TA's are great and most of the questions you may have might already be answered in the discussion forum, so do check it when you get stuck.

This course was very beneficial. It provided the entire spectrum of difficulty-from easy to quite difficult. I came into this course with a beginner to moderate level of programming knowledge and it proved to still be a mental battle at points. I would say a bit of advise would be to help clarify some of the assignment questions. There were several questions where I wasted a lot of time just because the prompt was not clear. I was able to figure out by asking or by looking through the discussion forums, though. But I think there may be some clarity needed to reduce the time of doing this going forward. I look forward to taking the next courses as well. Keep up the good work and continue to improve the course!

I thought the videos for this course were appropriately concise and well-done. The projects were about the right difficulty and length (although the one for week three was more time consuming than expected). They were interesting enough to keep engaged. I think the span of topics was also useful and appropriate. I would have liked if the autograder gave more detailed output on what was wrong with a submission. I also think it would be extremely beneficial to see the "right" ways of solving this problems after a submission is completed. I'm sure I didn't use the optimal pandas approach in some instances, as it stands I seem to have no way of knowing and improving my knowledge beyond getting a passing grade.

I like the content covered in the lectures. That's very useful for me to clean data parsed from webpages. There is one thing I hope the course can improved, which probably has been mentioned in the forum for many times: the auto-grader is not flexible that it doesn't grant any points to answers that is slightly different from the submitted form (e.g. upper/lower cases, white space, etc.) and we have a hard time figuring out what exactly the auto-grader is accepting. I would suggest modifying the auto-grader such that it allows flexibility in answer acceptance, or breaking down questions into smaller parts (more partial credits) and give more precise/detailed description on the format of answer expected.

There were some small draw-backs, like the fact that the subtitles in the lectures often had mistakes (it is clear it was either computerized or outsourced). But overall the content and the lectures were of very high quality. So I would recomment it to colleagues for sure.

However, there were some very frustrating omissions in the assignments that made them take much longer than needed. Lack of specificity in requirements, or even just not including very crucial details. I had to search the forum to find out that I was doing everything right, but just was doing not exactly the calculation desired. This is a big oversight and cost me time on the assignments that had nothing to do with my understanding.

In general, the course is present in a coherent and concise way. This course mainly focus on pandas library in python. Finishing this course, you will have the essential tool to manipulate pandas series and dataframe. However, the course has room for improvement, especially the assignment section. Question 2 of week 3 assignment is ambiguous and the data set provided is prone to misleading. And I also think the data set provided for week 4 assignment is corrupted. When set the house price data's index to ['State', 'RegionName'], the multi index is not unique! Also, few of the university town names are not in the house price list!

In a nutshell, if you are new to pandas, this is a good place to start.

The homework assignment really require a lot of research, which is daunting. It's not as if the things are that hard to research, but maybe sometimes some hints of where to start looking would be nice. I paid for many more months than one to complete this course, some of which was my unavailability, some was the quantity of time it took to churn through the assignments. OTOH, I had only coded in Netlogo and R, and it took a REALLY long time to understand slicing in python. It's not intuitive and it's not explained well online. How about some slicing questions for practice at the beginning, or separately from the practice? Or a practice module that offers endless slicing practice?