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Отзывы учащихся о курсе Introduction to Data Science in Python от партнера Мичиганский университет

Оценки: 25,656
Рецензии: 5,715

О курсе

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....

Лучшие рецензии


9 мая 2020 г.

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


28 сент. 2021 г.

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.

Фильтр по:

5301–5325 из 5,667 отзывов о курсе Introduction to Data Science in Python

автор: Shubhi v

25 июля 2020 г.

less hands on

автор: Sayyaparaju N V V V

9 мая 2018 г.

Nice one :)

автор: GIRIRAJ B

28 янв. 2019 г.

Good course

автор: abhishek

10 июня 2020 г.

very brief

автор: MariaStephan J

11 мая 2020 г.

very fast

автор: Arya P

2 июля 2020 г.

Too fast

автор: Weerachai Y

29 июня 2020 г.



18 мар. 2022 г.



28 сент. 2020 г.


автор: Govardhani S

6 авг. 2020 г.


автор: Aayesha N

30 июля 2020 г.


автор: Aansh S

10 июля 2020 г.


автор: Bicky G

13 июня 2020 г.


автор: GOWTHAM M

22 мая 2020 г.


автор: xiao h

22 окт. 2019 г.


автор: DELA C J K (

12 окт. 2019 г.


автор: Mohammad J

5 авг. 2017 г.


автор: Pranav P

17 июня 2021 г.


автор: Yash V B

20 мая 2020 г.


автор: Irfan S

4 окт. 2017 г.


автор: Richard H

29 июля 2019 г.

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.)

автор: Jeroen D

23 апр. 2018 г.

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.

автор: Neel N

3 сент. 2020 г.

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.

автор: Ryan N

19 нояб. 2017 г.

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.

автор: Jonathan T

3 мая 2021 г.

While my Python chops definitely improved as a results of the course, the homework was extremely frustrating. The requirements of the questions are not communicated in a consistently clear way.

What was more irritating, though, was that the auto-grader is extremely picky. There is very little room to solve the problem in your own way, and more of my time was spent trying to contort my code to fit what the auto-grader wanted than spent actually solving the problem and applying the course concepts.

I also was disappointed with how much we were expected to manually clean data. One of the questions even explicitly says that the answer will require students to "hand-code" the answer. This strikes me as an extremely poor habit to instill in students--combing through data manually to strong-arm the data into the formatting conventions won't cut it when tackling a dataset that is millions of lines long. For a computer science course, this is not a scientific, or even a programmatic, approach to solving problems.

I give the course 2 stars because I felt that only 40% of what I learned was data science and/or Python. The other 60% of what I learned was how to smash my code until it conformed to the auto-grader, how to bother the TAs in the forum when it wasn't obvious how to do so, and how to write translation dictionaries with the "wrong" format as the key and the "right" format as the value and then apply it to DataFrames.