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

4.5
stars
25,673 ratings
5,718 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 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

YY

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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3676 - 3700 of 5,673 Reviews for Introduction to Data Science in Python

By J

Apr 27, 2017

Content was generally well covered. One improvement could have been slightly better examples of how there are multiple different ways to solve the same problems; the instructor would often allude to this but would just advise the student to read through the pandas docs, which could be non-intuitive at times.

By MARCO S M H

Jan 9, 2021

El ejercicio de programación de la semana 4 de la sección 1, donde se hace trabajar con datos de estados, ciudades y equipos de deportes, me parece poco útil, demasiado manual y muy lento. Considero que es un muy mal ejercicio. Todos los demás estuvieron muy acorde a los problemas y la materia que se enseña.

By Haining Z

Apr 16, 2020

I learned a lot, but the assignments really could have been explain better. It was not clear to me what type of format/output I should give at multiple times. What I did was keep submitting while adjusting the output until it was right. A hypothetical screenshot showing what the result looks like would help.

By Joshua Z

May 29, 2017

Good course; a great intro to relevant concepts with guidance towards realistic tasks. Only negative is that sometimes problems are unclear (maybe its just me) and spent too much time trying to get outputs exactly right to pass auto-grader. Good experience, and I plan on continuing with the specialization.

By Monika D

Jun 12, 2020

I liked assignments they actually helped me to learn.Also book - Python for Data Analysis , 2nd Edition - Wes McKinney was great learning. I would just like to add in that if the videos can have better course material for Week3 and Week4 as assignments were bit higher level than what was included in videos.

By Harsh B

Sep 28, 2019

This course was nicely curated. I learned about the Python lambda functions,dates ant time,objects and map() in week 1.In week 2 and 3 I learned about pandas.In week 4 I learned about the distribution and Hypothesis testing in Python.In video quizzes cleared the doubts regarding fundamentals of the topic.

By Kamruzzaman T

May 31, 2020

One of the best courses along with a highly capable instructor. Very helpful teaching stuff and discussion forums. However, an assignment asked for the mean of a value but produced wrong answer for mean and showed right answer for sum. This needs to be reviewed. Also, many assignments need better clarity.

By Srinivas R

Oct 9, 2017

very good introductory course though with a steep outside the lectures learning curve. good for you as that is how much of learning takes place, not only through some one pointing the way but having a destination (hw, assignments etc) and finding your way to that goal along with others (discussion forums)

By Sam M

May 7, 2020

Good class and the topics covered are great. Challenging and useful. The lectures are a little light on detailed information and some text or deeper/more detailed lectures would make this perfect. You will spend a lot of time looking for things to support what you learn to really get an understanding.

By Kevin D

Apr 9, 2020

Videos are great and easy to learn. Forums are great and help with guidance. Improvements would be: better ways to debug your code and more importantly your results. Your code may get results but its not clear what is incorrect on them. Overall UofM produces some of the best work. Great professors!

By abdelrahman a

Oct 19, 2020

one of the hardest deadlines I ever did, missing some more hints for where the error is,

but really powerful makes you think in different ways as if you a real data scientist, I liked it

and I recommend it even if you were old in the field of data science, this course will really

make you try your best.

By Himanshu C K

Jul 7, 2019

I commend the quality of the course and am very thankful to the instructors for developing the contents of the course.

My only suggestion would be to align the lectures better to suit the level of the assignments so that people who don't have a strong programming background can keep up with the course.

By Vedant S J

Apr 24, 2020

The course was brilliant. It tried to cover a lot of aspects in a short span. The only thing that was not good was lack of explanation of the datasets & tasks provided in the assignment. Clarity in the expected output was not there as a result of which correct assignment submission became difficult.

By Ploy W

Aug 17, 2019

The course poses itself as a good brief introduction to make you more comfortable at approaching data science project. Recommended time would be 1-2 weeks. However, point deduced for the hassle with auto grader! I also hope that the project could involve people Skyping or collaborating in some way!

By Silvestre N F

Feb 27, 2017

Not for beginners.

Level of difficulty increase very fast.

You need to dig external reference sites and use discussion forums in order to figure out some assignments requirements.

Overall quality is very good.

Should have some better tips and validation scripts in order to help debugging assignments.

By Sascha U

Apr 17, 2021

Solid intro to Python for DataScience purposes. Definitely requires a lot of independent research and it's sometimes nearly impossible to achieve the assignment on the first go (everything is rated automatically, so even a slight deviation from the requirement format will lead to an initial fail).

By Ramon S

Oct 4, 2020

This was an informative and challenging course. I definitely learnt a lot, and would recommend anyone with some previous pandas and numpy experience to take it! The only downside for me was understanding the question, I spent more time trying to understand what I had to do than actually doing it.

By Anindya K

Aug 25, 2020

Please make the lecture materials more thorough. The assignments are very difficult and most of the time require learning from other sources rather than this course. I have sent more than 8 hours per assignment since I had to learn a lot of materials outside this course to answer those questions.

By Nitin K

Sep 7, 2019

The duration of videos is very small compared to the time needs to be spent to solve the assignments. There is a huge gap between what is taught and what is asked.Being a working professional with limited amount of time difficult assignments tends to reduce motivation to continue with the course.

By Mustafa S

Aug 3, 2020

The assignments are not well-prepared, TAs did really really good job though on clarifying vague concepts in discussion group. I expected to read assignments and complete them without digging discussion forums.

All in all it was a good class to learn new materials, but it is challenging though.

By Julie M

Oct 2, 2018

I really enjoyed this course, and gained a tremendous amount of knowledge about how to use python for data science (specifically how to work with pandas). I feel like there was a lot of advanced material, which is good, but makes the course quite challenging for an introductory level course.

By Andrej S

Jun 2, 2021

Steep learning curve, I learned a lot. Unfortunately the prepearation for the assignments was not as good as expected, though I read the readings, but the quizzes were ok. It would be nice to get the solutiions after finishing the course successfully, to gain insights into elegant solutions.

By Minh V

Aug 15, 2020

Quite challenging if you don't have alittle background in Python. Assignment is hard and time-consuming. The issue is the grader is old and sometimes I had to be forced to change my code for the grader to work. A lot of reseach and google if one wants to understand the logic behind the code.

By Ahmed O H

Aug 24, 2019

It's difficult as an intro to this science, I suffered so much to understand the codes and It forced me to take a small course about the libraries like Numpy and Pandas, I wish if this course more simply than it's.

At all, Thank you so much for your effort, I wish all of you the best forever.

By Rakshit T

Feb 25, 2018

Assignments and project were good and give you enough opportunities to self-learn a lot of data processing and cleaning tools. However the course video content does not really teach you much or in places, can leave you confused. Be prepared to spend a lot of time going through stackexchange.