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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
19,407 ratings

About the Course

Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement....

Top reviews

RM

Jun 9, 2020

Great course to learn the basics for Data Analytics using Python.I really liked the framework and data analysis template or process given in the labs. I will use them as a reference for my real job!

ND

Jul 30, 2021

T​otally overwhelmed with the course contents and easyness in teaching. The course will make you familiarize the fundamentals in a way that you will never forget when you used in a real world.

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2101 - 2125 of 3,071 Reviews for Data Analysis with Python

By KIM T

Feb 16, 2021

Great

By CS M L

Jan 26, 2021

great

By Shreya M

Oct 13, 2020

Great

By Rafiul A

Aug 4, 2020

Great

By Pankaj S

Jun 24, 2020

great

By Fernando P

May 13, 2020

Nice!

By saiganesh g

May 11, 2020

Happy

By Mauro M M R

Mar 23, 2020

great

By kuncoro a n

Jan 18, 2020

goood

By Diego B

Dec 18, 2019

great

By Arnab C

Aug 15, 2019

best!

By Kathleen P

Dec 31, 2018

Great

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Aug 29, 2025

good

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Aug 24, 2025

good

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Aug 3, 2025

good

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Jul 25, 2025

Good

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Jul 23, 2025

Good

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Jul 23, 2025

good

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Jul 21, 2025

good

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Jul 10, 2025

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Apr 4, 2025

good

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Mar 25, 2025

best

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Mar 18, 2025

good

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Feb 27, 2025

Good