Chevron Left
返回到 Introduction to Data Science in Python

學生對 密歇根大学 提供的 Introduction to Data Science in Python 的評價和反饋

26,484 個評分


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....




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



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 個評論(共 5,805 個)

創建者 Pragyan


Overall the course is fine. Much of the work is left out to the user, which would be a good thing if the lectures actually spent time discussing a topic. The instructor picks up a topic and shows us one example and is done with it.

I was disappointed with the teaching style. That being said, I did learn a lot in this course. I learnt a lot of stuff, but I wasn't taught much. Some of the topics were really interesting but they are concluded in 5 minutes max.

I really wish the programming walkthrough were more comprehensive and not just "here's how you do this thing, let's move on".

The assignments are challenging, but are poorly worded. Half the time I had to figure out myself what the assignment was asking me to do.

創建者 Mr. Q A


The assignments took too long for me to complete .

創建者 Jonathan J


great course, but the auto grader needs updating

創建者 hfculver


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.

創建者 Marc B


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.

創建者 Michael B


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.

創建者 Walter G


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.

創建者 Saeed V


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?

創建者 Deleted A


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.

創建者 YASH B 2


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.

創建者 Girija S


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.

創建者 Patrick H M


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.

創建者 rodania


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

創建者 amin s


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

創建者 Carlos L


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.

創建者 Yixuan H


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.

創建者 Adrián A R V


To be an introductory course I struggled a lot, is a very practical course, and the assignements encourage you to learn more. This is the best technical course I have taken. Lo recomiendo ampliamente

創建者 Andrew


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.

創建者 Kelam G


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.

創建者 Trish P


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.

創建者 David R Y R


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.

創建者 Marcel K


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.

創建者 Lorenzo V ( R P


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.



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.

創建者 Michael P R


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