Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7

stars

18,446 ratings

About the Course

Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models. Topics covered include: - collecting and importing data - cleaning, preparing & formatting data - data frame manipulation - summarizing data - building machine learning regression models - model refinement - creating data pipelines You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them. In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge....

Top reviews

May 5, 2020

I started this course without any knowledge on Data Analysis with Python, and by the end of the course I was able to understand the basics of Data Analysis, usage of different libraries and functions.

Apr 19, 2019

perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.

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951 - 975 of 2,884 Reviews for Data Analysis with Python

By Amandeep A

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Mar 11, 2022

Great course to begin your journey on Data Science with Python.

By Abduljalil A

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Jan 18, 2021

Great indepth explanation of how to gain insight into a dataset

By Nisakorn C

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Dec 7, 2020

Great, the contents are easy to follow are very well organized.

By Rivers L

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Jun 29, 2020

I really learnt a lot of data analysis skills from this course.

By Sherbulandkhan B

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Apr 14, 2020

Beautifully structured course. Highly recommend for beginners.

By Neelesh T

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Mar 5, 2020

I really enjoyed learning this course.

Thank You IBM & Coursera.

By Daniyar M

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Jun 19, 2019

great course to get an understanding of python for data science

By Xin W

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Jun 4, 2019

Very nice lessons for beginners to use python for data science.

By harshdeep w

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Feb 27, 2019

amazing course and the the problem question are just amazing!!1

By Praveen k

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Jan 23, 2019

great course to clear the basic analysis part. Great experience

By Xinwei H

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Sep 10, 2018

More exercise could be provided. Overall the material is great!

By Ronak P

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May 22, 2021

Explicit, Well Explained, Hands-on training using online labs.

By viet n

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May 16, 2021

this is an useful course I have a chance join. Thanks teacher!

By SIMRAN G

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Feb 3, 2021

Totally relevant and gives a feel of real-time logical anlysis

By Mojtaba T

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Dec 25, 2020

The training was very good and the quizzes were not difficult.

By João R

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Sep 28, 2020

Very practical and relevant for Data Science! Congratulations!

By Mohammad F

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Apr 26, 2020

This is a great course for learning data analysis with Python.

By Tanish S

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Mar 4, 2020

Superb for beginners for such those who are not able to afford

By David A C C

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Mar 2, 2020

English

An excellent course.

- Good methodology

-. Well examples.

By Matteo E

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Aug 1, 2019

It is a good course for beginners, topics are explained enough

By Moises G

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May 27, 2019

I learned new concepts and reinforce my statistical knowledge.

By Anna B

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Oct 13, 2018

Great course!

you get all needed material to analyse a dataset!

By Oluwasegun M A

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Jun 20, 2023

I got my first data analyst job after completing this course.

By Anurag S

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May 23, 2021

Really great course. Content and videos are really exemplary.

By PRUDHVI

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Jul 18, 2020

Heavy Brainstorming for Beginners and that's actually good :)