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

4.5
stars
27,081 ratings

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

CB

Feb 6, 2023

The assessments, quizzes, and course coverage are quite good. The main points are covered, although it does not cover everything. Additionally, it provides opportunities to learn and conduct research.

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4626 - 4650 of 5,952 Reviews for Introduction to Data Science in Python

By Anurag M

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Jan 6, 2020

Assignments are really good but the pace of teaching can be improved upon

By Bernardo J

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Dec 15, 2017

The assignment materials in the 3rd and 4th week need a SERIOUS make-over

By Ng Y H

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Nov 19, 2017

The lecture python examples could be closer to the homework requirements.

By Kevin D

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Jul 26, 2017

Interesting course with pretty useful interactive programming assignments

By Jorge G

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Aug 30, 2021

It covers necessary topics for my job purposes, but it's not sufficient.

By Karan K

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May 1, 2020

It was great experience learning the basics of data science with python.

By Daniel M

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Nov 18, 2019

Very clear and straight to the point, yet a bit advanced for a beginner.

By dibyaranjan s

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Oct 11, 2019

assignments are a bit tough, some of them are advanced than the teaching

By dhara a

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Jul 20, 2019

it is really nice course which gives you complete basic of data analysis

By Deleted A

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Mar 6, 2019

could have explained Hypothesis testing in better way with good examples

By AKI

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Jan 30, 2018

great course!but some assignments lack of clear instruction or mistakes.

By Carolina F A

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Oct 27, 2017

The course is pretty good. However, the tasks are no easy to understand.

By Mikhail

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Apr 5, 2017

Sometimes need more words in tasks, so it can help understand it better.

By Andrey Z

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Feb 5, 2022

Interesting course, with interesting practice assigments, i like them!

By ADITYA T

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Jul 4, 2020

Better teacher required.Language is very much technical,not simplified.

By Aditi T

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Apr 23, 2020

need to give more examples on specific topics for better understanding.

By QI S

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Nov 25, 2018

learn a lot, but the questions are so tough, i uses much time to do it.

By Akshay

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Feb 8, 2017

Course is good. I learned a lot. But the pace of teaching is very fast.

By Martin C

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Nov 28, 2021

Interesting, but I wish we could have the teacher's code for my notes

By Mohamed E

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May 1, 2021

great course for who want to take the first step in data science field

By Divya P

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Jan 26, 2021

Thanks, University of Michigan and Coursera for providing this course.

By Deepak G

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May 31, 2020

Well, the course is good if you are going for complete specialization.

By HARINI N 1

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May 9, 2020

This course was very useful, it helped me understand python in detail.

By Antoine W

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Feb 16, 2020

Learned a lot, but you constantly have to fight against the autograder

By Rajib M

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Oct 9, 2019

Course content is good but the explanations need to be more elaborate.