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

4.5
stars
22,734 ratings
5,095 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

SI
Mar 15, 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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4501 - 4525 of 5,019 Reviews for Introduction to Data Science in Python

By Nehal c

Jul 3, 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 25, 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 8, 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 4, 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 4, 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 6, 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 13, 2019

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

By Joel B

Jul 31, 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 5, 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 18, 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.

By Marat K

Nov 11, 2017

Much more time needs to be invested into theory of the data frames. The course is too lightweight for the heavyweight topic it's covering.

By SHUVA M

Sep 3, 2020

Course materials should be scrutinized. It's like the mentor is going through a scripted page. I understood very little from this course.

By Tobias T

Aug 26, 2020

Good course for the basics, but the assignments are very difficult as lectures do not cover everything which is asked in the assignments.

By Greg S

Jan 4, 2018

Great Content. Course Auto-Grader was immensely frustrating. Videos aren't very helpful except to identify where to do your self study.

By Sai S B

Jun 19, 2020

The course assignments are at a very good level. But, I feel the course doesn't prepare you for that. Most of the work is self-learning.

By Kelsey S

Aug 17, 2018

The examples used are so small it's hard to understand how to use these skills in real-world situations if you aren't as used to Python.

By Michał Ż

Jan 5, 2018

There should be more Pandas API hints in lectures, it ware really hard to find optimal ways to perform operations on DataFrames I wanted