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学生对 IBM 提供的 Data Analysis with Python 的评价和反馈

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19,631 个评分

课程概述

Analyzing data with Python is a key skill for aspiring Data Scientists and Analysts! This course takes you from the basics of importing and cleaning data to building and evaluating predictive models. You’ll learn how to collect data from various sources, wrangle and format it, perform exploratory data analysis (EDA), and create effective visualizations. As you progress, you’ll build linear, multiple, and polynomial regression models, construct data pipelines, and refine your models for better accuracy. Through hands-on labs and projects, you’ll gain practical experience using popular Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, SciPy, and Scikit-learn. These tools will help you manipulate data, create insights, and make predictions. By completing this course, you’ll not only develop strong data analysis skills but also earn a Coursera certificate and an IBM digital badge to showcase your achievement....

热门审阅

UA

Jul 28, 2020

AN excellent course. Hands-on training on the cloud makes an individual really involved. So far the best online course I have ever taken, and I have learned Python programming a lot from this course.

BM

Jul 16, 2020

Although good to learn the know-how of basic data analysis techniques, the quizzes are predictable and you don't end up coding as much as you should. A good starter course to wet your feet in DA!

筛选依据：

2126 - Data Analysis with Python 的 2150 个评论（共 3,119 个）

创建者 JAIPRAKAS G

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Oct 9, 2022

good

创建者 SONALI M

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Oct 6, 2022

good

创建者 Nightcore E

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Jul 15, 2022

tuyệt

创建者 Amol C

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Sep 6, 2021

great

创建者 bilibil

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Feb 17, 2021

10/10

创建者 KIM T

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Feb 15, 2021

Great

创建者 CS M L

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Jan 26, 2021

great

创建者 Shreya M

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Oct 13, 2020

Great

创建者 Rafiul A

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Aug 4, 2020

Great

创建者 Pankaj S

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Jun 23, 2020

great

创建者 Fernando P

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May 13, 2020

Nice!

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May 11, 2020

Happy

创建者 Mauro M M R

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Mar 23, 2020

great

创建者 kuncoro a n

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Jan 18, 2020

goood

创建者 Diego B

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Dec 18, 2019

great

创建者 Arnab C

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Aug 15, 2019

best!

创建者 Kathleen P

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Dec 31, 2018

Great

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Jan 31, 2026

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Dec 14, 2025

good

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Nov 21, 2025

well

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Nov 10, 2025

Good

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Oct 30, 2025

good

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Aug 29, 2025

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Aug 23, 2025

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Aug 2, 2025

good