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

25,758 ratings
5,733 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


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.

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3501 - 3525 of 5,684 Reviews for Introduction to Data Science in Python

By Matt M

Dec 19, 2016

I think the structure of the lessons are highly relevant for data sciecne and taught well. So for Syllabus content I think you are doing veryy well

Cons: Nearly all the problems I encountered were related to the autograder not being helpful! There could be better error handling/debugging i.e. instead of just saying the answer is wrong, explain why and the aswer that is expected. For example, I spent DAYS on the final question (run ttest)of the last assignment. I had the right answer all along except the autograder was expecting the type to be numpy float 64, whereas my answer was just a float!

By Zbyněk F

Apr 10, 2021

Course content is great and so the way how videos are made. Issue (at least from my point of view) is the test and evaluation part. I love challenges and the 1st test I really enjoyed, but for the next 3 tests it was not possible to finish whole test without reading additional information in disscusion section for each week. I consider it as annoying. It would be great to add some extra informations into the description of questions. Evaluation is kinda problematic since sometimes you manage to get the required output but it is still considered as wrong. Other than that very nice, thanks :).

By jie

Apr 24, 2020

What I like this course:

Assignment! Geat assignments. Challenging and learnt a lot.

What I do not like this course:

instruction videos: way too shallow, and no real examples. I understand pandas and numpy are both very comprehensive libraries and it is impossible to cover every details. However, if the instructor could teach a real case study,(like similar cases in assignments), learners could have better understading going into the assignments.

Finally, teacing staff are great, very helpful.

I learned a lot from this course, not from the instructors, but from assignments and teaching staff.

By Federico A

Jan 21, 2017

I can see a couple of problems in this course. How to access and change elements in a data frame (especially the use of loc and iloc) is not explained very well and I believe there is an error in the slides. The second thing is that I have found more powerful methods in pandas documentation, for example assign() or nlargest() and nsmallest(). This is not really a problem if one of the "hidden" purposes is to push students to actively search the docs for what they would like to do.

Work load for the assignments is just fine in my opinion, not too little, not too much.

Overall I am satisfied.

By Jeff B

Nov 9, 2017

I felt that there was very little hand-holding in this course. I think there was good and bad from that. On the one hand, I think it could have been more helpful if the lecture content more directly correlated to the assignment questions. However, I definitely think that learning to utilize the internet (stack overflow etc.) is an integral part to coding in any language in today's landscape as well. So there is something to be said for that for sure. Overall, I think I learned a lot of valuable skills in the course and I'm looking forward to continuing the others in the specialization.

By Saadat A

Jan 22, 2021

I enrolled 4 years back this course but that time got some issues as some video lectures were not much clear or comprehensive (not the lectures from Christopher Brooks). Besides , submitting assignments were much easier as I got required assistance from staff , specially I want to thank Yusuf Ertas. I wanted to give 5 starts but given 4 stars as some of the questions/problems in assignments were really confusing to understand where the actual task/question was quite simple to solve . This issue frustrated me badly and costed me to extend course accomplishment time twice.

By Weinan H

Dec 11, 2018

It is a very nice course, providing you are able to pour a LOT of individual efforts into it, especially into the assignments. The knowledge required to finish the course is far beyond what they taught. I spent 70% of my time in the course on Stackoverflow or Google, or asking my friends. However, I admitted I gained huge amount of knowledge and experiences, as well as "individual research skills" and confidence, from this course. I would recommend going through this course in a much shorter period of time than recommended (like in a week), during your vacation or week off.

By Sally L

Oct 30, 2017

Solid introduction to using Python for basic data science applications. By the end of the course I feel confident that I know how to go about cleaning data and doing some basic analysis on it. The practical programming assignments are great, you can really dig your teeth into them without them being so challenging that it scares you off. Expect it to take you longer to complete the assignments than the official estimated time.

What's also great is that all the code from the lectures is posted so you can easily play with it to be able to follow along with the lectures.

By Will K

Mar 10, 2018

I didn't utilize the discussion pages until the third week, but they are extremely helpful. The pseudo code that was sometimes given in those discussions was also helpful for learning the material because it revealed programming solutions that I was unaware of given my inexperience with the python language. My only real issue is that I wasn't expecting the self-learning for the end of week assignments, but it wasn't that bad. The in-video problems were nice, I would like to see more small, non-graded assignments to help practice the language and get a feeling for it.

By Tallula J

Sep 28, 2020

This is a really great course. It's super informative, the video lectures are more engaging than those of any other online class I've taken, and I have learned so so much. However, I started this course as more or less a complete beginner. If that's you, then be prepared to spend a ton of time and energy on the homework (around 6 hours per assignment). I fortunately had a friend who helped me out a lot, but if you're a complete beginner, short on time, and don't have access to someone who can help you, I'd recommend finding another course or working up to this one.

By Blake P

Jun 29, 2020

CONs: I did find some of the ungraded peer evaluations could use a little refinement. I've shared this feedback with the team though and I doubt it will get in the way of anyone else's learning.

PROs: Found the forum useful and between that and stackoverflow, getting the hang of Python for Data Science shouldn't be any trouble with those coming from an analyst or programming background. The ethics discussions and articles presented were good foundations for folks starting out in a field where communication of risks and biases is critical before acting on results.

By Jakob K

Dec 4, 2016

This course is accurately advertised as requiring intermediate programming experience. It focusses on some of the basic python tools that are used in data science like pandas, specifically dataframes. For a python newbie like me, some of the assignments were quite challenging as they required significant additional reading and search. I would have appreciated if all of the concepts required to solve the assignments were introduced in the same week, so my additional reading and search would have been more targeted. Very little stats if any is required or taught.

By Alabhya P

Aug 14, 2020

Was a great overview of the Pandas and Numpy frameworks.I loved the course as it gives a quick overview to every aspect of pandas framework.But, what I absolutely hated was that the instructors just introduce you to every aspect and just do it themselves without much of an explanation, specially after week 2 ends.So, we need a lot of self studying which is super overwhelming to those of us who are just being introduced to data science in Python.Self-studying is a key aspect, but still a bit of more explanation in each video would have made the course 5-star.


Nov 12, 2018

I really like the lectures. They are fast paced, full of useful information. I also like the design and intentions of the assignments. They really help enhance the learning. I love the required reading too. It really expands your vision from a course to a much bigger world.

My gripe is mostly on the lack of attention to details for the assignment questions. There is a lot of ambiguity there. Forums and tips helped, but I would rather they spec out the questions in the first place. I hate to guess what the auto-grader want.

All in all, it's a very good course!

By Peter S

Nov 30, 2018

Loved the lectures and structure. Great course. The assignments were very educational, but I was a bit frustrated with the autograding at times. It felt like, what was preventing me from advancing had nothing to do with the actual course material, but the technical set-up instead. I don't know if it's possible, but having the auto-grader tell you what went wrong (which criteria your submission didn't live up to) would be a huge help and help students like me avoid too much frustration. Just a thought. Still a great course - keep up the good work :)

By Rohan S

Apr 28, 2020

I found this course very useful and interesting. Assignments are very helpful to test your skills and how one can apply the content learned during lectures in actual problems. There is a lot of functionality and details in the Pandas library but, this course is designed very well to enable the students to get a good understanding of Pandas. Although, I faced some problems regarding the assignment grader. Moreover, the course videos end abruptly and it gets difficult to understand what the Professor was explaining towards the end of the lecture.

By Bruno L

Jan 26, 2017

Com certeza o curso melhorou o meu conhecimento em python. Antes mesmo de terminá-lo já aplicava os conhecimentos no meu trabalho. Contudo, terminei trabalhando muito mais tempo nas provas do que o tempo estipulado pela equipe de instrutores. Cheguei a pensar que estava fazendo tudo errado, mas vi nos fóruns que outros também tiveram a mesma dificuldade. Estou muito satisfeito, não quero provas mais fáceis, apenas uma noção mais adequada do tempo que será gasto (levando em conta que estrangeiros podem demorar mais para entender o enunciado).


Jun 25, 2020

The videos were little fast paced. The explanation were good. It would be better if more detailed explanation on pandas has been given. Some more functions of pandas must be given because in assignment part the questions were little tricky and the question of that type has not discussed. You have to learn more from other resources. However overall course was good, it gives a good explanation on how to use python to manipulate data in table and many more. Before opting this course the individual should have a good grasp on basics of python.

By Faris

Jan 28, 2017

A lot of the harder questions were unclear or not covered by the course. Many were passed onto "look this up yourself", which is fine, but sometimes a different approach resulted in a slightly different answer marked as wrong. The group_by stuff was especially tricky, as it was unlike its SQL counterpart and could have used a better example, eg with a function to split into quarters or group columns. Other than that, very interesting overall and very neat datasets. I would encourage having students check out for extra credit.

By Dusan Z

Mar 5, 2020

Small criticism - either requirements.txt should be published (version dependencies) or the course should be updated for to reflect modern versions of pandas, or perhaps both. Autograder has a tendency not to be able to process subtle differences in dataframe shapes from time to time and this is discussed at length in the forums but nothing much is done to rectify. Possibly related to obsolete versions and dependencies. Questions could be slightly better formed. All in all, a great course, which has kept me busy for a couple of weeks!

By Bhavin P

Nov 11, 2018

Introduction to Data Science in Python is a nice introductory course to jumpstart your career in the field of data-science while working with python. The course assumes you have basic knowledge of python programming including its syntax and semantics. Later, the course quickly dives into topics like list comprehensions, lambda expressions, numpy arrays, pandas series/dataframes. Most, of the common tasks involved in the field of data-science such as data cleansing, data analysis and manipulation are thoroughly covered in this course.

By Robert S

Dec 6, 2017

More about Python skills related to Data Science than Data Science itself.

Good quality of lectures, although they are very dry and brief. Assignments are challenging and require some googling/extra study. Supplementary materials are very inspiring - I enjoyed them the most.

If you want to be well prepared to use Python in data science field and have some time to spend working on the assignments - this is the course for you.

If you want the gentle introduction to the whole discipline - this is not the starting point I would recommend.

By Francois

Nov 20, 2016

This was way above my level knowledge. One will definitely a strong level of knowledge in stats and data analysis to begin with. The Python part is but only a tool in this module / course.

For that reason I found the tutorials way too fast paced. I had to go back on everything and test, so a 5 mins tutorial video would sometimes take me 30 mins or longer to complete.

The 3 hour / 4 hour assessments took me days!

Yet - a great way to jump into something new! The course itself is great. The presentation and content is just as great.

By Danteswara R P

Oct 28, 2017

Excellent course challenging at every stage. Especially the emphasis on assignments without spoon feeding is really brilliant.

One suggestion is during the assignments, especially week4 there are situations where Pandas was giving lot of trouble. For beginners it is becoming trial and error in between. Will be good if you can collate such challenging aspects and include optional tutorial for students to specialize in those topics. YOu need not cover everything but some comprehensive on such topics. Otherwise course is excellent

By user n

Feb 6, 2022

Overall I have enjoyed the course and got useful skills from it. I really appreciate the effort the course development team has put to create it. It would be better if the programming assignments include more 'textbook problems' where we get to practice the basic uses of python (eg. rename, groupby, apply) before delving into these long and hard real-world problems. Sometimes I just got so frustrated when I found out I did not actually know how to carry out the basic operations while doing the assignments.

Thank you!