I LOVED the content but the assignments were WAY too hands-off for my taste. The lack of video explanations for how to go about approaching the assignments, and lack of written instructions, lead me to feel like I was spending hours upon hours teaching myself instead of learning from someone. That may be a great way to teach but it is very time-consuming and not ideal for busy students. My other gripe was that the automatic grader was very unforgiving for even slight variations, e.g. I spent approximately 2 hours converting my PeriodIndex values for assignment 4 from pd.Period('2001Q3') to strings in the form of '2001Q3' and then had to lowercase the 'Q' to 'q' before I received a passing grade which I felt was a large waste of time.

The videos are to-the-point and there is a lot of great exposure to field of data science. The assignments are instructive and very realistic I think. The forums are well monitored and the staff does help.

HOWEVER- there is much to be done with the course grader. The course does not support assignments in the latest versions of pandas, which is a real headache. I'm at the point now where I have done everything except the last question on the last assignment, and I'm just going to move on to other things in my life. Because I have the latest version of pandas and I don't want to spend the time to figure out how to use the one the grader supports. Also, I did well learning the material, but my intuition for a statistical ttest is weak.

the learning environment and the scripts are very nice, they're very easy to read and to comprehend, and have a lot of insights. The same can be said about the assignments. They're a steep curve from the materials covered in the lessons, but are related and can be done. The classes, however, are a (very) weak point. Instead of going through the coding, doing slowly some examples and explaining through the codes, the lecturers are, in practice, just citing the classes notebooks. In this sense, there's no need for the class. Moreover, although they indicated this is not a beginner's class into Python, it is not an intermediate or advanced either, so the lectures should be paced more slowly and more detailed on the coding.

A course which has great assignments. However, the video itself is a bit boring. Most of the time, my motivation to learn this course is just doing its assignments.

At the same time, the assignments are somewhat difficult for those who are not familiar with Python, but for me it's just OK. What I want to make complaints about the assignments is that sometimes the Autograder is so rigid that I have to try one question over and over again until the Autograder "feels happy", and for me, sometimes the gap between "correct answer" and "incorrect answer" isn't so large...

And finally, thank you, teacher Christopher Brooks! You are ateacher full of passion, and I actually learnt a lot from you and your course.

Lectures were interesting and well put together, however the assignments and the knowledge required for the assignments were not covered in the lecture material. While I can appreciate that every course will require some elements of self-learning and exploration, this felt a step too far. My sense is that if you have some experience in the actual topics covered by the course, and are looking to verify your knowledge with a certificate, you will be fine. However if you are hoping to actually learn about the topics, you are going to have to work very hard. I'm hoping that the coverage of course material to assignment requirements is a lot better in the subsequent courses in this specialization.

The course definitively succeeded in motivating to struggle with Python, using different tools to search for solutions and trying out different ways to solve certain questions. However, this was mainly achieved through the assignments which appeared not to be 100% in line with the lectures (what was needed was not touched in the lectures or only after the respective assignment).

Whta is additionally challenging is that the Jupiter Notebooks for the weeks and additional trainung notebooks created by the user on the one hand and the assignment notebook are using different versions of Python. This is kind of unintended additional training (trying to identify alternative ways to answer the questions).

The course content and assignments are pretty good at covering the fundamentals of how to manipulate data in python, I did learn a lot that I can see my self applying practically.

Where it falls down though is the assignment auto-grader. It's immensely fussy (as I understand it has to be), but the assignment instructions are often very vague and don't sufficiently explicitly define whats expected. This means you spend more than 50% of your assignment time debugging and recasting objects and getting very frustrated whilst doing so.

I would recommend this course, but I do hope that the course administrators pick up on this feedback and make the assignments clearer in the future.

This course would be great if you are experienced in Python or any other programming language. The title ' Beginner Specialization. No prior experience required.' is very misleading and if you do not have any experience, you will struggle a lot especially to complete week 3 & 4 assignments.

It makes you think and requires a lot of self-study, which is a good way to enhance our knowledge and skills, however assignments are way too challenging for those who are 'beginners' or have little real-world experience. I would not recommend if you do not have much confidence in your programming 'experience'.

Having said that, teaching team is great - very knowledgeable and supportive.

For me the course's biggest value-adding point is to simply ask us to Google everything on Stack Overflow to complete all the assignments, which has almost nothing to do with the course content. The assignments are also badly designed. In the discussion forum you can see lots of confused / frustrated posts. The TA's and mentors are clearly under-resourced to answer all our questions. The videos unfortunately don't teach us much either. The professor + TA's a simply reading off a teleprompter, and they're rushing through everything. I've done some amazing courses on Python also offered from Uni. of Chicago, but this course has been a big disappointment unfortunately...

This course provided a brief introduction to using Python for data science applications, with a strong focus on pandas method. It is well-suited for students with some background in Python programming. The assignments are rather challenging. It would be great if there are more in-video questions to reinforce our learning and prepare us for the assignments. Working with the autograder required tremendous patience. It would have been a better experience if there was test code to help us debug our errors. On the whole, I benefited from this course, but I must point out that successful completion of the assignments required a lot of independent learning and patience.

I learned so much. However, the lack of support when I got stuck made it a frustrating experience. I would not have been able to pass without the discussion forum tips. Even then, it was a lot of mucking in the dark and assignments took longer to complete than they should have. I'm glad I took the course but it was not designed for someone at my skill level. If the discussion forum was organized in a better way, it would have helped -- I had to scroll through pages of text to find the right tips to identify my mistakes. Sometimes the question requirements were oddly or ambiguously phrased and I misunderstood the expectation for the function being tested.

I appreciate why the data cleaning and debugging steps are included - I imagine this is a key component of working with real world data, but I think the time I spent debugging and cleaning could be better spent purely manipulating the data to get the answers to the questions in the assignments.

I don't think the introductory videos on python are necessary - they would not be enough for someone to do the rest of the course. I would replace that with explanations on how to use jupyter notebook and getting more from the course in that way.

In all i enjoyed this course, I particularly enjoyed Week 4's lectures on hypothesis testing.

This course is a decent introduction to using the python pandas data science package, but suffers from some problems.

The lectures are very brief and do not prepare you well for the assignments. The assignments are not well described and the autograder is very finicky. As a result, every week I spent several hours on the fora and stackoverflow to figure out what the autograder wants, instead of actually learning pandas. I managed to pass this course with (what I know is) subpar code, but we never get to see proper solutions to the assignments.

The course has potential, but as it is now, I will continue this specialisation.

This is a decent course. My constructive feedback:

-It is pretty light on actual videos and the instructor moves a little too quickly through them

-Some of the questions in the assignments aren't as clear as they should be which can result in time spent trying to figure out the question instead of working through the answer

With that said, if you have an intro level background in python and enough motivation to finish the course this is a great resource for getting acquainted with Pandas. If you don't have an intro level background in Python and some understanding of objects this will be a real challenge.

The structure of the course is clear. I like the assignments that requires individual learning. Though it is time-assuming, I got much sense of achievement after finishing them. However, what is annoying is that the instructions of assigment and the feedback of automatic correctiong are not specific enough. It takes me too much unnecessary time to figure out what is wrong with my submission. One example is that the automatic feedback failed to tell me that I mixed up True(boolean) and 'True'(string). I finally realize it after viewing the teaching staff's reponse to others' questions in the discussion.

I learned A lot from this course, and I am grateful, for the effort. tutors and professors designed the course and set up autograder, delivered lectures so I appreciate all that. Having said this, though, very often end of week assignment questions are VERY ambiguous. I would say Not well communicated and very confusing. If one examines the forums for this course one could easily corroborate my notes . HOWEVER, that ambiguous nature of assignments made me work hard, search for answers, and try harder, for which I am also THANKFUL ). 3 stars is for the unclear formulation of the assignment questions.

The assignments were interesting and challenging, but not nearly enough elementary examples were provided in the lecture videos. The in-video quizzes appeared immediately after a new concept was introduced and didn't leave enough time to think and process. For some assignments, it wasn't clear how much of my work should be based on the lecture and how much should be based on independent internet searching. I wish I had learned more about standard conventions for writing certain kinds of code given that the instructor discussed some ways being better than others but never gave sufficient examples.

A mixed bag, mostly because of the very poor programing assignments due to very poor task descriptions and monosyllabic answers from tutors when asking in the discussion forums. Wondering what could be the precise meaning of the exercises leads to hours of trial, error and frustration.

Besides this the content of the course is good to understand and execute data cleaning and prepare real world data from various sources for further processing like ML. Pandas is at the center here.

I wanted to take more courses from the Michigan Python series but I will not do this due to the issues mentioned.

I came to this course after finishing the 5-course series by "Dr Chuck" Python for Everybody and with no other Python or much else programming experience. It sounded that this is the optimal course to build on the Python for Eevrybody sequence. But it is a huge jump in difficulty, and for a learner like me the lectures way way too fast, and included a lot of specialised programming terms. Also the assignments are hard and often not very well formulated. I am not saying that this course can't be done when just coming out of the Python-for-Everybody series, but it is very demanding.

I believe that this course is one of that courses that make you realize what you are stepping into, My expectations were hurt because i thought that Data Science was more math involved and had awesome statistics involved, oh boy was i wrong. The course majorly deals with data cleansing and copying a lot of code from stack overflow. if your python is not good please don't take this course otherwise you will get frustrated. If you have planning on taking this course be prepared to be grilled this course will require perseverance.

And most importantly the lectures ,they don't help!

The instructor is warm and seems pretty interested.

Content is way too minimal though and it's not enough to prepare the student for the assessments.

A lot of searching is required in order to arrive at the solution which more often than not is not the most optimum one. Forums are only supported by fellow students and staff's involvement is next to zero in there. Even threads that deal with clarifying questions were not answered by the staff.

I understand that this way enforces community building and helping fellow students, but staff's participation would be highly appreciated.

The material felt very brief. Felt like this was suppose to be a refresher course. The lecture videos are not very helpful in its delivery. I wished there were more visual aids to help me understand the lectures more. Most of the time, it's just the lecturer sitting there talking and some quick screens of the Jupyter codes. I walked away with a brief overall idea of the material instead of an in-depth understanding of the concept. The assignments were challenging and I felt like they were very helpful. Expect to spend a LOT of time researching for the assignments.

This course was challenging taking into account that I don't have a lot of experience in phyton. I'm not going to lie, i would have prefer that the videos and the lectures give us more tools to solve the challenging assigments. I have to confess, I spent a lot of hours solving these assigmments, firstly, they are not easy and secondly, the auto-grader doesn't give you a real feedback on which you can work to fix the code. The way I see things, the autograder needs to improve and the content of the course will be better if it is more related with the assigmments.

I learned a lot through this course, in particular searching the docs and skimming stackoverflow. It was very helpful. I do hope though, that the grader and materials will be updated in the future.

It caused me annoyance to battle with grader. It was not grading properly what in my offline jupyther-notebook runs just fine. I hope that this part of UX, or better - SX (student experience) will be mastered, so that students would concentrate on learning and not on trying to submit the assignment to the obsolete grader. Please, do solve this problem. It matters.

Course material is good esp. the assignments force you to learn a lot more. However, the instruction is not comprehensive. A few assignments were also ambiguous. The support forum is quite good but it would have been much better 1) if instructors would cover key topics a bit more in detail, 2) Easy to find auto grader scripts so one can understand the error or provide a better feedback mechanism from autograder. I had a spelling error in the answer and it took me 4 hours to correct it. Once I had the autograder code, the bug was very easy to catch.