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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

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.

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

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1051 - 1075 of 5,951 Reviews for Introduction to Data Science in Python

By 317126511138 K S R

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Apr 5, 2020

This was very useful and have learned many things from this course and by learning this course this creates enthusiasm to me to earn more about data science.

By Diego J R L

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Mar 23, 2019

Excellent course a good way to learn Pandas API in 4 weeks. Beware the assignments, read the discussion for pointers or common errors before submitting them.

By Arend Z

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Jan 19, 2018

Great teacher, great lectures, good example code and assignments that are not too easy and not too hard. I really feel like I learned and achieved something.

By Jorge R

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Jan 17, 2018

Excellent for data science beginners. You must know python programming and some background in statistics beforehand in order to fully understand the content.

By Tu H

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Jun 7, 2017

The course is great for senior data analyst. However, you need to learn by yourself probability and statistics because they do not cover them in this course.

By Yi-Yang L

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Feb 4, 2017

Perfect courses! With a lot of opportunities to practice and to learn from do questions by yourself. Mentors and staffs are also kindly to answer problems.

By Brian L F

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Nov 19, 2016

Excellent class. Needs a little work to make sure assignments a little more clear, but with a bit of work from the student, it's a great learning experience.

By Jesus D M C

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Jun 11, 2023

This course was great, it makes the pandas libraries easy to understand and the assignments were hard enough to make the student strive and were practical

By Dani D

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Feb 20, 2021

Week 4 should be covered more thoroughly or should have additional suggested reading materials when statistics has not been students main area of expertise.

By Lucas C

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Dec 29, 2020

Fantastic course that uses real data. The assignments are the best part of the course, which is where a lot of other MOOC courses I took are really lacking.

By Usma B

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Aug 2, 2020

the python friendly nature in data science is flexible and easy to use large big data. its introduction and function as been useful to know the information.

By Shreyash R

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Jun 29, 2019

The courses are phenomenal with a little introduction to the topics and then encouraging the students to explore to do the assignments. Simply magnificient.

By Sanjay N

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Apr 29, 2018

I am very happy with the course. It was not easy. Quick paced and needed a lot of groundwork and reading but that is how it should be. Thank you very much.

By Fahad A

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Sep 23, 2017

They way of teaching is perfect. I think the students can grasp the concept more easily and permanently through this approach. Good effort!

Wonderful course.

By MARCELO A R M

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Oct 16, 2020

A very useful Course to start with Data Science in Python. I'd learn to manage the basics libraries in py and something about data cleaning and statistics.

By Benjamin C

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Oct 28, 2019

Fast paced and interesting lectures, challenging project + I learned something about economics with the final assigment!

Thank you Pr. Brooks and your team!

By Bomin L

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Sep 22, 2019

It was great lecture! I really enjoyed studying pandas and python with real data set.

It was a bit hard to use skill right after I learned, but it worth it!

By Yufan Y

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Sep 12, 2019

The course is well structured. It not only taught programming knowledge but also raised some issues that we need to consider when conducting data analysis.

By Jan W

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Mar 11, 2019

Solid intro to Data Science. It made simple for me to go through elementary Data Science concepts and is a best course I have taken in this topic so far <3

By ozkanserttas

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Mar 12, 2018

Highly recommend taking this course to people with intermediate Python background. Especially, the assignments are great and very well prepared. Thank you!

By Luke G

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Mar 3, 2018

You're gonna spend a lot of time reading documentation if you're not familiar with pandas, but it's a good course that really makes you learn the material.

By Clément S

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Jun 14, 2017

An excellent dive in Python and Pandas. Christopher's course combines fast-pace and clarity; you never have to wait for the video to move forward. Thanks !

By Ascánder S

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Mar 7, 2023

Excellent. Very challenging if you are not experienced with Pandas or Statistics. You'll start to become comfortable with Pandas by the end of the course.

By olatunde o

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Jul 17, 2022

seriousely this course is awesome! I'm be part of this course as a student and i wish to dive more. My gratitude goes to the entire instructors. Thank you

By Utkarsh G

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Jun 29, 2020

It was a nice insight on the topics and methods which are being use for data cleaning,data integration and data analysis with their meaningful extraction.