The course content was OK. It's jut a pity that the assignments section was so frustrating to use. I needed around 10 attempts for each submission just to realize that I forgot a print() or that the file names were not exactly as in the requirements... In total, I spent half of the time allocated to assignments just to hunt for stupid errors.

The course was helpful in presenting typical problems in data science, but the lectures showed you examples in one area of pandas and the assignments asked questions in different areas of pandas. I found the assignments frustrating and spent much of my time trying to understand what they were asking rather than actually solving the problems.

The course is good but needs a bit more comfort with programming concepts than what the "Python for Everyone" course provides. Also, the course length could be slightly longer - a lot of material seemed to be rushed with not enough time to really grasp the concept before moving to the next one. Still, it laid a good foundation to build upon.

The assignments in the course are designed to really test your skills and knowledge. However, I did not find the lecture videos to be much useful. For the assignments, I had to look most of the stuff on the internet (Stack Overflow and Pandas documentation). The course is definitely worth it if you are ready to spend time on the assignments.

The content of the course is reasonable. However, the time needed to read documentation and forums is not factored into the estimated hours needed to complete the course. Lastly, having to sign up for the whole series just to take one course is money making racket. And then you have to cancel the other courses which is very inconvenient.

Great and detailed course, but very confusing and possibly inconsistent 'beginning of recession' question in assignment 4. After much research online and note of teaching staff definition of the quarter to look for, I still got it wrong according to the autograder. But strangely got everything else right that depended on this answer?!?

Though the information and knowledge provided was a very effective and great insight, I did feel that the course was a bit rushed and fast-paced. Big topics cannot be understood with a 3-minute video that barely touches the theory and gives only one example. Practice should be accommodated before we enter those difficult assignments.

The course is interesting, but the grading system needs to improve substantially. The grader bugs considerably, and the questions are often not well defined, which requires a lot of unnecessary trial and error to understand what exactly the question was about. Finally, complete the assignments took way long that what it is suggested.

Participants should be familiar with numpy and pandas because the teaching pace is very fast. The assignments are long questions which make them sort of complicated. Even so, there is no simpler practice to familiarize yourself with numpy and pandas. I will get back after reading the textbook or watching helpful videos on YouTube.

It is an interesting course that shows you part of the extent of the things that you can do with the Pandas library within python. However don't expect to be able to code everything by scratch, instead expect to be able to google the answers for your coding questions and be able to adapt those to your particular coding objectives.

I am currently taking this specialization and I find it very practical. However, I suggest that the exercises be updated regularly. Please note that the exercises should test creativity rather than being simple questions that can be asked in a chatGBT, and the use of a chatGBT should also be considered as part of the curriculum.

The assignments are great and challenging.

Lectures leave a you bit short or not fully equipped to take on segments of the assignments, that I understand and accept however I feel it they should hit at least what else do you need to go out and learn yourself in order to complete the assignment.

Oh and the grading system, eish.

Good and necessary introduction to Python. But especially Assignement 3 regarding cleaning the data should be improved. It´s good to know how to clean the data but it´s confusing when Q1 is apparently correct but actually it´s not. Mabye it would be better that Q2 - Q13 based on a new dataframe which is correctly by all means.

The lectures do not contain enough material to prepare you for the programming assignments. Programming assignments are challenging but the problems are not clearly stated, most of your work will consist in finding answers in Stackoverflow instead of looking for the answers in lectures or programming assignment instructions.

While the information provided is not bad and I liked the style of teaching, with the Jupyter Notebook, the assignments are unnecessarily ambiguous, to the point that you have to go through the forums simply to understand the meaning of the question and what to return. And the autograder mechanism was also quite misleading.

the instructor guides students to pandas, not bad. the assignments are not difficult, but are poorly designed---it's hard to get the goals of functions, and one may not get them done without searching internet. just don't know what a function is supposed to return, and there's no enough explanation on the assignments.

The Assignments are very good. But the video lectures are terrible. they offer no or very little Explanation what so ever of the notebook being displayed on the screen. The TA just reads the notebook instead of diving into what the functions are doing. The subtitles overlap with what people are writing in the journal.

This course was over all okay. My primary complaints are that I felt that the class moved too quickly and relied too heavily on students to teach themselves through the Pandas documentation. The Pandas documentation is really only so-so and it would have been nice to have more guidance through the course materials.

the weekly assignments are so challenging.But the instructions are not clear enough.for each question,one has to adventure through the discussion forum to find what the question expects.Thank you for the discussion forum because there are enough good people to explain and ask questions in each and every dimension.

Though I very much appreciate Dr. Brooks' traits when video lecturing: relaxed, informative, and often lucid-- I worry that students with little to no computer science background will struggle greatly with assignments. That is to say, there is a disparage between the videos and the expectations of the assignments.

Contents are great and very relative. Exam is fair and reasonable. However students have to deal with an autograder for the scores and the autograder is not up to par for this course. The amount of time that you spend on learning during the course is only a fraction of time you spend to get through the autograder.

The assignments have bugs that become more and more present after browsing the discussion board so extensively. I think that it would be really helpful to eliminate any bugs in the grader, so that when you get a solution marked as correct, you can actually count on later problems building on a correct solution.

The course doesn't feel complete, the information and techniques used for assignments can be found completely online through documentation and instead this is merely an exercise for doing basic analysis through documentation rather than an explanation of python through data science (which is what I anticipated)

This course is a very nice course, though it wasn't close to being thorough, but it helped me to develop self learning skills and endurance in tackling problems. it also helped me to have a pattern of study for data science, providing me with assignments which tasked me and helped me in learning so much more

Easy to Understand, but there's some errors in the course. So go check Discussion when you get confused.har

Assignments is very very time consuming, especially when you're not an American, like me :(. The data cleaning process is somewhat tedious but I think it'll be similar to the messy data in real life.