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Learner Reviews & Feedback for Introduction to Data Science in Python by University of Michigan

4.5
stars
21,868 ratings
4,906 reviews

About the Course

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....

Top reviews

PK

May 10, 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

SI

Mar 16, 2018

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

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4526 - 4550 of 4,833 Reviews for Introduction to Data Science in Python

By Daya_Jin

Jul 20, 2018

作业描述不清楚,有歧义!!!

By Shubhi V

Jul 25, 2020

less hands on

By Sayyaparaju N V V V

May 09, 2018

Nice one :)

By GIRIRAJ B

Jan 28, 2019

Good course

By abhishek

Jun 10, 2020

very brief

By MariaStephan J

May 11, 2020

very fast

By Arya P

Jul 02, 2020

Too fast

By Weerachai Y

Jun 29, 2020

thanks

By Govardhani S

Aug 06, 2020

good

By Aayesha N

Jul 30, 2020

Nice

By Aansh S

Jul 10, 2020

good

By BICKY K

Jun 13, 2020

nice

By GOWTHAM M

May 22, 2020

good

By xiao h

Oct 22, 2019

太难了8

By DELA C J K (

Oct 12, 2019

HARD

By Mohammad J

Aug 05, 2017

good

By Yash V B

May 20, 2020

ok

By Irfan S

Oct 04, 2017

A

By Richard H

Jul 29, 2019

Truly horrible delivery of the material - even worse than Coursera's old Intro to Machine Learning course from Univ of Washington. This course will discourage nearly anyone from pursuing Data Science.

And it's not even an intro to data science. It's a course on Pandas for dataset manipulation. (In fairness, cleaning up ingest data is like 95% of the work in data science, but the course doesn't even tease the student with some exciting machine learning examples of where this is all headed.)

It's not delivered like you'd expect an intro course. It does an awful job of progressing the student through the Pandas toolset, building concepts incrementally. The whole topic of object types, methods, returned objects, and chaining gets barely a mention, but it's essential to the assignments. Examples are rapid-fire and sparse - very few techniques needed in the assignments can be found in the examples. The Week 2 quiz tests on techniques not introduced until Week 3, and the Week 3 and 4 assignments cite "individual study" which is academic-speak for "We didn't teach you about this - go Google it".

Then, there are errata that the student needs to pick out of the discussion forums to pass the assignments because some key questions are vague. The errata are 1-2 years old and they can't be bothered to correct errors.

The auto-grader could be the highlight of the course, but it provides limited feedback on wrong answers and no guidance toward the right answer; just "wrong". You're not allowed to post code or discuss answers in the forum - you have to go to StackOverflow to do that. (It'd be awesome if several of the exercises provided the student with the answer and challenged them to match it, but instead it's very sink-or-swim.)

Even when your answer is right, the auto-grader throws errors and warnings for, say, returning a numpy.float64 (which you should) when the grader is expecting a Python float type. Or it's expecting a float64 for a counter value (!!) when you provide an int64 (which is correct). These behaviors should have been fixed long ago.

It claimed to be a 15-hour course; I did it intensively and invested more than 30 hours before pulling the plug on the final project. That was claimed to be a 4-hour project, but experience with the rest of the course says it'd be more like another 12 hours - and that's for a guy who's not new to coding.

Bottom-line: I paid for educational material and I don't feel like this course delivers. What it does deliver is Pandas exercises and an "OK" auto-grader; truthfully, most of what I learned was via Google searches while trying to do the assignments - effective, but very slow and very frustrating. The real disappointment is seeing that the issues I encountered have been well-known for 2 years in the discussion forums; the course could be a lot better by now if they cared to nurture it.

Finally, a frustrating aside that's on Coursera, not the instructors... Coursera's online Jupyter notebook platform is really unstable and constantly drops connections even when you're actively editing and executing cells. (Including from 2 Fortune 100 companies - it's not the connection.) Once dropped, the notebook can't be re-connected, and has to be re-launched from the syllabus at the risk of losing your most recent edits. (But beware, if you run Jupyter offline for stability, this course also has defective input filenames that will cause grading to fail - read the discussion forums first.)

By Jeroen D

Apr 23, 2018

More or less my copy from an earlier review,

I was really excited about the this course, and was really let down. This course is really, really poorly done. I would not waste time and money on this course when there are much better options out there. I feel like I've gotten little in return for my time and money.

First, as several other students have noted, the timeframe for assignments is really unrealistic, taking much longer than projected (at least for me, and several other students). This is not acceptable when Coursera bills by the month. Coursera needs to provide a better assessment of the time commitments for the class. I took another datasciense course prior to this one (my employer wants a certificate) but still the assignments were tough, and I found it really dissappointing that I spend a lot of time solving inconsistencies in the assignments. I believe American students are in advantage here because of the Geo-American orientated datasets.

Second, the teaching is horrific. The professor is not engaging at all, but simply mechanically reads lines which often sound straight out of a user manual. The point of online videos is not to turn books into audio files- it’s to have a human talk/reason through problems with you. The teacher of the course should discuss the material, not recite a manual. In addition, the little amount of material is presented far too quickly, Also great emphasis is put on the discussion groups (which turns out to be just responded by the moderators, volunteers). In absence of a proper syllabus students are directed to Stack Overflow, a sign of the courses' weakness.

Third, the title of this course is a misnomer: an introduction to data science would provide an overview of the tools, techniques and scope of the field. An extremely detailed introduction to Pandas, which is essentially what most of this course is, is useful if well executed (which it is not here), but it is not an introduction to data science.

A more minor complaint is the absolutely horrendous choice of the background. Showing different permutations of lifeless office drones is not exactly inspiring material for aspiring data scientists, even if this the reality of office life- it’s distracting at best, and at worst, deeply disparaging. Why not have just a plain colored background? Or anything else?

The only positive thing besides some of the misleading assignemnts are the rest of the assignments. In general I had fun solving them, and althoug I've had my share of Jupyter Notebook and Grader's issues I was able to complete the course. I will not reconsider any online course from Michigan University again.

By Neel N

Sep 03, 2020

It pains me greatly to give just 2 stars to a course from UofM, since it is my alma mater, but I will be honest. I would like to echo the sentiment of the majority of my fellow learners that the course needs to be structured better. Instructor needs to take more time to explain some of the concepts in greater detail. It seems like the instructor and his assistants are always trying to rush things and cover too much material in tool little time . I had to pause and replay lecture videos to completely grasp what was being conveyed. I also adjusted my playback speed to 0.75x to keep up with the instructions. I will admit that I had to heavily rely on the pseudo codes posted on the forums to answer assignment questions and even though I answered them correctly, I did not completely grasp the reasoning behind lot of them, which I think defeats the purpose of learning a programming based course.

Suggestions for improvement: Upgrade the autograder, because it is frustrating to keep rectifying the answers to make them acceptable for the autograder. Completely overhaul the assignments so that they are more in-line with what is being taught in the lectures. Students should not have to figure out everything from the online forums. If not for the pseudo-codes, algorithms and explanations from mentors, this course would have been an impossible one to finish. Assignments and exams need to be designed such that learners don't have to treat forums and stack overflow as a primary vehicle for getting successful with the course, but more like a helper tool.

By Ryan N

Nov 19, 2017

The course content is very good. The videos are very good. Unfortunately this course is severely hurt by a very high ratio of non-learning work to learning work. This is due to some issues that could be easily addressed. The questions are poorly worded or ambiguous about critical details. Some of these details are hidden in the forums, but that's a waste of time. Some of the assignments do not directly bear on the course content and involves much "self-learning". Unfortunately this means I do not know if my self-taught methods are optimal - there is no feed back or checking. So you can do very poor coding but still pass in scoring and never get any feedback to improve your coding skills. All along, some very simple hints about what libraries and methods to use for each question would prevent lots of blind searching on the web. There are some helpful instructors and helpers haunting the forums, but they are not always around, and they are not always implementing permanent fixes to the problems that are frustrating students. One shouldn't have to hunt around forums to find out about broken pieces of the application or other errors in the course. Finally, the grading system is unstable and the Jupyter Notebook system is also not very stable, leading to many submissions and resubmissions just to make sure it got through for grading. For these reasons it took much more time than three weeks for me personally. I would not have signed up had I known.

By Alan S

Oct 13, 2017

The topics covered in the course are certainly important for anyone interested in using Python for Data Science, but sadly, there is only really basic information about each topic taught in the videos. In the labs, there is heavy focus on "self learning" (basically, the instructor encourages you to use StackOverflow and other resources to figure out things that were intentionally not taught in the course).

While it's interesting that the problems have a very real-world nature to them (including searching the web to help you find answers to things), if I wanted to learn the tools taught in this course that way, I wouldn't have enrolled in this, and just got a good book and practiced myself independently.

Also puzzling is that there are weird "discussion" segments that have no relevance to any of the topics taught. One moment you are learning about pandas dataframes, and the next, it's asking you for a 90 word opinion on data science ethics. This is somewhat ironic given the otherwise very practical/applied nature of the course. Not relevant at all to a working professional.

One other note: as I write this, for the past few days, no students were able to get their assignments graded, since the auto-grader was broken, and there are multiple reports of this in the forums. It's puzzling the staff let this problem persist for days with no ETA or acknowledgement of the issue.

By Kevin Y

Feb 08, 2017

Overall, this course is tough for me. In my opinion, the course title is not suitable, which is "introduction". The word "introduction" generally means something basic and fundamental. However, the level programming assignments in this course is between slightly medium and very hard.

Furthermore, the assignment instructions are not clear enough, i.e. what steps need to be followed to complete each question. So, it needs time for me to find the way or solution in the discussion forum. I know in each week forum, there is one discussion specifically for assignment (i.e. Assignment X Tips), which is good. But, I think the forum is not a good place, since we have to find it meticulously the information and what we want to find is spread out, not in an easy to follow order.

Moreover, I do not think the lectures and the assignments are well synchronized as there are many things in assignments that are outside from the lectures. I mean, it is "too many" new things in the assignment. Although it is a great way for the students to search and enhance their skills by themselves, in general, the assignments should at least reflect the lectures.

Lastly, in my humble opinion, this course is not appropriate for someone who is new to programming and has not been familiar or mastered any programming languages, though the title is "introduction".

By George A

Jul 16, 2019

This is a pretty awful course, as of the time of writing this review in July of 2019. Let me preface this by saying that the material you learn is very helpful. Pandas is a great library to learn for loading, cleaning, and manipulating large amounts of data. But the real problem with this course isn't the material, it's the lectures and the autograder. The lectures are very short. They don't cover the concepts well enough, and some material is blatantly skipped and you have to learn it yourself through google. Then comes the video quizzes which test you on functions and concepts that haven't even been introduced yet. It's like the quizzes were put at timestamps randomly. Then comes the worst part of the course: the Autograder. I'm not sure how old this course is but the autograder is running on an outdated version of both python and pandas. What does this mean for you? Well if you want to code on your computer instead of the course's broken online coding notebook, you will run into severe code-breaking bugs between versions. It really ruins the course. I learn the material but then spend hours trying to please the broken autograder. Most of the time in this course isn't spent learning, it's spent fixing code that the autograder rejects even though it runs perfectly on your machine locally. Have fun!