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学生对 密歇根大学 提供的 Introduction to Data Science in Python 的评价和反馈

25,938 个评分


This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....



May 9, 2020

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans


Sep 28, 2021

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.


76 - Introduction to Data Science in Python 的 100 个评论(共 5,724 个)

创建者 Mr. Q A

Dec 26, 2020

The assignments took too long for me to complete .

创建者 Jonathan J

Apr 16, 2019

great course, but the auto grader needs updating

创建者 hfculver

Apr 4, 2017

Dreadful course. Instructors saw no value in presenting elements of course that would help learners complete the assignments; rather you are sent off to teach yourself about uncovered techniques needed to complete the assignments. From some of the posts from previous students on GitHub, they resorted to deriving the answer from another means (Excel?) and simply providing the answer as a constant value, in order to receive credit for particular questions. Not exactly sterling knowledge transfer, from instructor to student! This course should be presented as a challenge course to people that have already learned Python Pandas from some other venue. (BTW, Pandas documentation is also dreadful, as of this writing.) This is definitely not the way to learn Python for Data Science if you are a busy professional software engineer. (Wish I had a good recommendation as an alternative.)

The only positive aspect of this course is the challenge to work with defined datasets, to complete specific tasks, during week 3. (This was as much time as I could afford to allocate to this course.)

From a 40+ year software engineer, with doctorate in CS, a part-time instructor at a private university, with a very challenging technology job in a multi-national corporation.

创建者 Marc B

Jan 11, 2019

The assignments are good practice, but the course teaches you nearly nothing. You have to do your own research to figure out how to do them.

There are some very useful Mentors on the forums to help the assignments, and if it were not for them, this course would be unbearably frustrating and useless.

创建者 Michael B

Mar 3, 2020

Video lessons go way too fast and don't actually try to teach you anything. If you're already a wiz at using Python to do data analysis, then you could certainly keep up, but then you wouldn't need the course in the first place. Very poorly paced.

创建者 Walter G

Nov 18, 2020

This is not an introductory course! There is a very large assumption that you already know a lot of about the pandas library, as well as extensive knowledge about dataframes and series.

创建者 Saeed V

Sep 19, 2020

This course is a real waste of time! Please avoid!!

The lecturer in general teaches nothing. He explains some basic concepts. You can learn them in a 5 minutes YouTube video. Then, you should answer the detailed/technical coding assignments. The assignments have nothing to deal with the lectures. The lectures have zero to very limited coding explanation. Then, there is an outdated picky auto grader that grades your work. You will spend hours finding out that your code is correct, but the auto grader works with libraries very old versions. I learned nothing from the lectures but I passed the assignments with 90, thanks to StackOverflow and online resources.

I am wondering who gives this course 5 stars. Fake reviews?

创建者 Deleted A

Nov 19, 2016

The jupyter notebook made this a horrid experience. Plus Coursera really doesn't want you to bother them with your silly questions, relying on peer-forums. If you scroll through the week's discussion forums, many student posts go ignored.

You can't drop the course past the second (I guess) week so the system will keep on keeping on long after you've given up on trying to figure out the janky notebook thing.

Will not return to Coursera for any reason. Breathtakingly bad experience.

创建者 YASH B 2

Oct 4, 2020

The course lectures hardly covered what was asked in the assignment. For someone who has a full-time job scouting through discussion forums is extremely time consuming.

创建者 Girija S

Nov 2, 2020

Too much content condensed into 4 weeks of course. The videos are very fast with ~1.5 hrs every week and do not cover what is being asked in the assignments at all.

创建者 Patrick H M

Nov 12, 2020

Slamming down some notebooks is not teaching. Despite this shortcut does the lecturer still miss to show and explain the difficult cases of the different concepts.

创建者 rodania

May 8, 2017

One of the worst course I ever take in coursera. The instructor just writes codes on front of us without explanation.

创建者 amin s

Dec 4, 2019

terrible course please improve teaching efficiency and give a proper realistic assignments

创建者 Carlos L

Oct 26, 2020

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.

创建者 Yixuan H

Sep 29, 2021

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.

创建者 Andrew

Feb 11, 2019

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.

创建者 Kelam G

Jan 17, 2019

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.

创建者 Trish P

Apr 29, 2019

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.

创建者 Ainur A

Nov 28, 2020

The course was undoubtedly challenging (which in itself is a good thing), but at times it was more challenging than it had to be because of some little errors, inconsistecies, wording in the assignment, etc. Sometimes some files wouldn't be where they were supposed to be. Sometimes it took me more time to actually understand what I was supposed to do than actually doing it, because it wasn't really clear what was required. Ok, English is not my first language but I am quite good. Besides, lots of other students complained about confusing wording, so it's not just me. Sometimes some instructions were missing and you'd only realise that after digging through the discussion forums for hours. So, basically there were a few things that made the whole experience more difficult than it had to be, and they weren't about knowledge or skils but about the course "infrastructure".

Before I started this course I'd already taken a few courses on Python programming (on a different platform), including numpy and pandas, but the assinments in this course were still qute difficult for me. You have to do a lot of searching online to get to the solution. In general, I'm all for the idea of having to do more self-learning but the online format is already more challenging than face-to-face learning, so I think it would help to have a few guided exercises between the lectures to understand the material better. Maybe if I had more experience with Python and programming, it would be easier.

One thing that disappointed me was that they recommended a book (optional reading), but you would have to purchase it. Now, if we were studying on campus then we would have access to the university library where we could borrow this book (probably), but as online students we don't have that option. I understand that they can't just upload copywrited books for anyone to access but maybe they could come up with a system that would allow students to somehow access the books temporarily, or make just a few necessary chapters available. I think that's for Coursera to manage, not for the University of Michigan though, because the university can only take advantage of whatever technology coursera can offer (at least that's my understanding).

On a positive note, the discussion forums were quite helpful, and the teaching staff were quick to answer most of the time. I suggest, if you take this course, then take full advantage of the discussion forums.

I also liked that there were two types of assignments (multiple choice quiz and programming). I think that's a great idea.

So, overall, if they made a few improvements to the course, it would be a great one.

创建者 David R Y R

Nov 2, 2020

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.

创建者 Marcel K

Apr 19, 2019

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.

创建者 Lorenzo V ( R P

Jan 7, 2021

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.


Nov 7, 2020

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.

创建者 Michael P R

Mar 21, 2019

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

创建者 Daniel S

Jan 18, 2021

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