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Learner Reviews & Feedback for Data Analysis with Python by IBM

4.7
stars
12,014 ratings
1,738 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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1551 - 1575 of 1,719 Reviews for Data Analysis with Python

By Marta I C

Aug 23, 2020

This is a good course for beginners with Python. The content is explained in a very direct and comprehensible way, but more programming exercises and tasks are required.

By Ying W O

Sep 27, 2019

There are lots of typos in the labs and assignments, which can be frustrating. I expect better quality from IBM. Content is great and easy to understand nevertheless.

By Matteo T

Jan 01, 2020

This course is quite good. The bad thing is that the arguments of the last "lesson week" are treated very superficially, taking for granted some advanced knowledge.

By Marcel V

Jun 28, 2019

A lot (too much maybe) is covered in this coarse

It really helps a lot when you know some statistics. Like linear regression,

Why gridsearch was covered I wonder.

By Dylan H

Apr 03, 2019

While a bit fast and loose with the concepts, does contain a lot of practical code as to how exactly to bring things discussed about, which is appreciated.

By Xuecong L

Feb 16, 2019

Thanks for teaching me the systematic way to do data analysis! However, I found quite a few mistakes in the lectures in this course, hope it will improve!

By Hao Z

Aug 12, 2019

IBM Cloud is difficult to use.

The generated link of notebook will not share the latest version, if you click the share icon before editing the notebook.

By Neo B

Feb 11, 2019

Data visualization was taught in details in course 7 and regression was taught in course 8. With no backgrounds, the codes in this course are scaring pe

By Goh S T

Apr 08, 2020

The section on model development and evaluation is not so clear. It is difficult to understand if you have no prior knowledge of machine learning.

By Girgis F

Dec 31, 2018

Course was great however i felt a lot of material was covered in a short period of time, this course can be 2 or 3 courses based on the content

By Guillermo M M

Aug 20, 2018

It is missing a last project like in the two other courses... It would have been quite fun to be able to apply what we learnt in a project.

By A P

Jun 14, 2019

Lots of spelling and grammatical errors that made it difficult to understand some of the material. It is otherwise an interesting course.

By 靳文彬

Mar 11, 2020

There is no slides to download or review after class which makes it hard to go over the knowledge again without watching the video again

By Siwei L

Jan 23, 2020

The video is too short and many concepts remain unclear or poorly explained. More contents need to be added to the questions and tests.

By Carlos R

Mar 26, 2020

It's an excellent course; the best part is the labs. It's a pity that we (the students) have a lot of problems loading the labs.

By Pedro F

Aug 22, 2019

Little bit confusing, unstructured and not easy to follow. Material inside is good though, but the course needs to be improved.

By sangeet a

Apr 08, 2020

Things are too fast in this course and many things remain unexplained which makes it difficult for one to understand properly

By Dominic M L C L

Sep 16, 2019

Too many errors in the code and explanations. Makes it very difficult to understand which is the right procedure/conclusion.

By Adam J L J H

May 24, 2020

This course focuses a lot on the theory and explanation. However, there isn't much hands-on practice for the coding itself.

By Osama W

Aug 25, 2020

*No response to some questions/comments on the forum

*More details/thorough clarification required for some points covered

By Rishika A

Mar 26, 2020

There are many errors and this was even the toughest course I have taken yet since many things were not explained clearly

By Kuzi

May 06, 2020

Course is flawless but when i had a technical challenge the Coursera team were clueless on how to fix it.

Otherwise good.

By Akash T

Jul 11, 2020

Few of the video requires improvement in terms of its quality. Particularly the lectures corresponding to week 4 and 5

By Teofilo E d A e S

Apr 16, 2019

Too complex for easy understand. Should have some documentation explaining the process and comparing the new methods.

By Vrinda M K

Nov 26, 2019

Topics covered are important but videos end abruptly as if narrator was saying something more and video just ended