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

4.5
stars
22,527 ratings
5,048 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

AU

Dec 10, 2017

Wow, this was amazing. Learned a lot (mostly thanks to stack overflow) but the course also opened my eyes to all the possibilities available out there and I feel like i'm only scratching the surface!

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4451 - 4475 of 4,969 Reviews for Introduction to Data Science in Python

By Chan W Y J

Jan 24, 2018

You'll spend more time struggling with the jupyter notebook (assignment platform) than actually writing or learning code. The lectures are really good, though.

By Yan X

Nov 04, 2019

Great content. But some assignment questions are not that clear and might cost you more time than its worth. And feedback from mentor is not that responsive.

By Abhijit G

Apr 27, 2018

The course is well designed and assignments are complex. What I did not like about this course is that the assignments are not well explained with examples.

By Narayan S

Aug 17, 2020

The main problem is with the auto grader. There are too many issues making it cumbersome to get the assignment submission right in one go. Please fix this.

By Parth M

Jul 12, 2020

Had to learn most of it by myself. Got discouraging at a certain point. Should have informed about the prerequisites.

Learn Numpy, Pandas before enrolling.

By Ryan T

May 18, 2020

Some parts were quickly rushed through and poorly explained. However, they did explain the bare bones of pandas, which was the main reason for this course.

By Pengyue S

Jul 01, 2018

There is one critical technical problem lying in the assignment three and already caused hundreds of students' grade blank in the forum, including myself.

By Nehal c

Jul 03, 2019

As a beginner I found it a bit of a brisk over the topic. There was a lack of basic questions. But in the end I was coping up and then the course ended.

By KUSHAL B

Jul 14, 2020

too fast in explaining it was bit difficult to keep up with the explanation,small code example were taught but assignments questions was too difficult

By Aram M

May 26, 2018

Great course material, but the autograder system was frustrating to work with for assignments, and often made me less motivated to work on the course.

By Himansu A

Jan 16, 2019

The course is okay for beginners as it is having only few lecturers for basics. Coursera experience was good. Overall i am satisfied with the course.

By Yaseen H

Sep 24, 2018

The assignments are not even close what is being taught. We are taking this course so we get everything in one place. Curriculum has to be improved

By Souvik B

Jun 08, 2020

Not at all for beginnners. Fast-paced with more focus on self-learning and grinding,rather than focussing more upon the concepts. Dry presentation.

By Konstantin K

Mar 04, 2018

Quite bad knowledge delivery from lectures. The course is rather self learning than course. A lot of vague points and uncertainties in assignments.

By VARUN K

Mar 04, 2017

The course instructor could have been more elaborate with the examples. I felt there was a wide gap between the exercises and the course material.

By Justin L

Dec 06, 2016

Assignments are challenging, but some questions are very vague and require lots of trial and error guesswork to get the autograder to accept them.

By pouya S

Jun 29, 2018

Assignments are great to reinforce your learning. But the instructor does not cover many topics and leave you with a lot of questions unanswered.

By Hanwen L

Aug 15, 2019

Please update the auto-grader such that is it compatible with current version of Jupyter notebook, very frustrating dealing compatibility issues

By huta

Aug 14, 2019

This course is a nicely organized. However assignments are not completely clear. Especially assignment 4 needs more explanation and details.

By Joel B

Aug 01, 2019

Subject matter was very good. Some of the assignments were not clear on instruction, and some of the Coursera functions were buggy or broken

By Paul A

Nov 05, 2018

Material delivered a bit too rapidly to effectively assimilate. Often, further external research is needed to find solutions to assignments.

By Anant A

Mar 27, 2019

I don't think this is a good enough course to "teach" you "data-science". All this does is give you an overview of things you need to know.

By Ahmad A

Jun 24, 2018

The assignment descriptions needs to be precise (with psuedo code).And the statistics part needed a lot visualization to aid understanding.

By Jordan K

May 19, 2018

The material is valuable and taught well. The lectures are impossibly fast paced (lots of pausing) and the assignments are often ambiguous.

By Vipin G

Dec 16, 2017

Great Assignments, Great learning, but requires good "prior" knowledge of Python and Pandas. This is more of a refresher course in Pandas.