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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

SY

May 22, 2020

Data analysis with python has a high standard of content.I hope this is useful for me to crack DS interviews.The content is well structured.Quizzes,assignment and video lectures are corelated.

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!

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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

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Aug 4, 2020

Great

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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

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Mar 23, 2020

great

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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!

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Dec 31, 2018

Great

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Jan 31, 2026

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Dec 14, 2025

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Nov 21, 2025

well

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Nov 10, 2025

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Oct 30, 2025

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Aug 29, 2025

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Aug 23, 2025

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Aug 2, 2025

good