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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
7,329 ratings
903 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.

OA

Jul 13, 2018

I have been looking for a very non-complicated course on data analysis and I hit the Jackport with this course! Very simplified and explanatory. You should definitely take the course

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701 - 725 of 897 Reviews for Data Analysis with Python

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 Obong G

Feb 19, 2019

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

By Rohit S P

Apr 25, 2019

Needed a more brief explanation on ridge regression and grid search

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 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.

By Rahul S

Jul 18, 2019

Very Helpful course..and very good contents..learnt alot..

By Narayanaswamy N

May 25, 2019

First go for module 8 - Machine Learning and come to this.

By Filiberto H

Jan 17, 2020

Very difficult if you don't have some statistics bases

By Serena R T

Nov 22, 2019

A tough course yet interesting. Like the lab exercises

By Ran D

Jan 05, 2019

The question jumped up in the video is quite annoying.

By Andres E S G

Jan 11, 2020

It could have a little more theory about statistics.

By Adesua A D

Nov 04, 2019

My first course on coursera and its very informative

By Alexandru S

Jun 03, 2019

A lot of information, it is at times hard to follow.

By Neelam S

Jan 03, 2020

Examples should contain more codes used frequently.

By Zijiang-Yang

Oct 01, 2019

it might need updating according to the new version