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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
12,075 ratings
1,746 reviews

About the Course

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate....

Top reviews

RP

Apr 20, 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.

SC

May 06, 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.

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1401 - 1425 of 1,726 Reviews for Data Analysis with Python

By ZHANG B

Apr 23, 2019

The content in the lab is great! However, video courses are not so good.

By shiva m a

Aug 03, 2020

Awesome introduction to data analysis with python. Loved it absolutely!

By معاذ ع

Oct 11, 2019

some topics have not been covered well like piplines , cross validation

By David O

Jul 26, 2020

The materials are well-organized, but there are many typos throughout.

By Angeliki M

Dec 02, 2019

A really good course. Probably the best so far in the IBM Certificate.

By Nanjun L

Jan 09, 2019

Would be better if more programming-oriented assignments are provided.

By Rahul P

May 13, 2020

Excellent course with detailed hands-on experience via lab exercises.

By Manas C

Mar 31, 2020

The course covers all the fundamental concepts needed for a beginner.

By Obong G

Feb 19, 2019

Though found the ending modules a bit challenging, its a great course

By Padraig M D

Jun 07, 2020

Quite a challenging course, but very rewarding. I really enjoyed it.

By Rohit S P

Apr 25, 2019

Needed a more brief explanation on ridge regression and grid search

By Ninad M K

Jul 14, 2020

It is a great course and it teaches me data analysis with python.

By Frank G

Mar 19, 2020

I think that for weeks 4 and 5 the course needs more explanation

By Wen P

Dec 25, 2019

Easy understanding

Good sample and comprehensive

Good for beginner

By Jeff J

Aug 27, 2019

Nicely explained. But many minor mistakes here and there though

By Bashar M

Feb 05, 2019

thank you very much ,this course was very useful and interesting

By Cherif H W A

Dec 14, 2019

as usual the labs are great but the videos could be much better

By Nicholas J F

May 04, 2019

Good content. Still spelling errors and mistakes in some place.

By Mudita N

Feb 20, 2019

Last few weeks were a bit confusing but overall a good course .

By Ismayil J

Nov 05, 2018

Good overview of classic Statistic methods performed in Python.

By Shine

Jun 18, 2019

There are something wrong in the final assignment submit page.

By Kuznetsova T

Jan 25, 2019

Great course, but the number of errors in videos is tremendous

By Yongda F

Jun 16, 2020

This is a good course for beginners, but not enough in-depth.

By Eliezer A

Jul 30, 2019

there are some errors in the code lines through the lecutres.

By WANG T

Jan 24, 2019

Typos in the videos and notebooks should have been corrected.