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学生对 IBM 提供的 使用 Python 进行数据分析 的评价和反馈

11,549 个评分
1,652 条评论


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....



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.


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.


551 - 使用 Python 进行数据分析 的 575 个评论(共 1,648 个)

创建者 Nipol C

Jul 26, 2019

This course provides you with good introduction to data analysis.

创建者 Prasanna P

Jan 18, 2020

Excellent assignments to take challenged work at the end of week

创建者 Joshy J

Oct 31, 2019

One of the best course in IBM Professional certification program

创建者 Ioseb M

Dec 21, 2018

Great Course. It took some time to be finished but it 100% worth

创建者 Rivers L

Jun 29, 2020

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

创建者 Sherbulandkhan B

Apr 15, 2020

Beautifully structured course. Highly recommend for beginners.

创建者 Neelesh T

Mar 06, 2020

I really enjoyed learning this course.

Thank You IBM & Coursera.

创建者 Daniyar M

Jun 19, 2019

great course to get an understanding of python for data science

创建者 Xin W

Jun 04, 2019

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

创建者 harshdeep w

Feb 27, 2019

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

创建者 Praveen k

Jan 23, 2019

great course to clear the basic analysis part. Great experience

创建者 Xinwei H

Sep 11, 2018

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

创建者 Mohammad F

Apr 27, 2020

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

创建者 Tanish S

Mar 04, 2020

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

创建者 David A C C

Mar 02, 2020


An excellent course.

- Good methodology

-. Well examples.

创建者 Matteo E

Aug 01, 2019

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

创建者 Moises R G G

May 27, 2019

I learned new concepts and reinforce my statistical knowledge.

创建者 ANNA B

Oct 13, 2018

Great course!

you get all needed material to analyse a dataset!

创建者 Pasam P K

Jul 18, 2020

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

创建者 Russel A

Jul 15, 2020

Excellent Lectures. A wonderful experience learning Analytics

创建者 Anuj K

May 22, 2020

structured course to get started on data analysis with python

创建者 Azman A

May 04, 2020

Great Content , balanced and well delivered to guide learning

创建者 Vibhor G

Apr 29, 2020

Much needed course for those who are from a different fields.

创建者 Friscian V C

Jan 20, 2020

best course so far! lot's and lot's of information, loved it.

创建者 Siddharth C

Jan 13, 2020

The whole course was very interactive and easily understable.