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

4.7
19,533 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

LM

Mar 9, 2020

Very good course that goes straight to the main topics needed to work on data analysis using Python. This will kick start my learning process which will be followed with a lot of coding practices.

TG

Sep 5, 2023

Wonderful course, explained everything in the most easiest way possible. BUt hte module 4 and 5 ffeels kinda hard for a person who is not familiar with data visualization and machine learning.

筛选依据:

876 - Data Analysis with Python 的 900 个评论(共 3,098 个)

创建者 Nikhil J

Apr 18, 2020

Neatly structured. Great course. Will recommend to anyone interested in DS.

创建者 Sergi R

Apr 15, 2020

Muy buena visión de los pasos a seguir para generar un modelo y verificarlo

创建者 Hashum A

Apr 7, 2020

this course is very excellent they give you depeath knowledge of statistics

创建者 Revalda P

Jul 24, 2019

This course is great, it covers the foundation of Data Analysis with Python

创建者 Junaid A

Feb 20, 2019

More than sufficient overview of SQL and how to use it in jupyter notebook.

创建者 Jano D G

May 24, 2024

Very useful tools to expand your statistical analysis and machine learning

创建者 Elnaz J

May 25, 2023

Very well organized and approprite assignmens along with many useful labs.

创建者 Rhonda M A

Jul 8, 2022

Great Course with ample opportunities to utlize the material via the labs.

创建者 Omar E

Jun 21, 2022

IT'S A VERY HELPFUL COURSE FOR ANY BEGINNER IN DATA ANALYSIS USING PYTHON.

创建者 Harsha K

Sep 9, 2021

Very very happy with the course thank you IBM for this amazing opportunity

创建者 mohammad f a

May 1, 2020

Awesome course data enthusiast must take this course . Best course content

创建者 Johann F R P

Apr 19, 2020

Excelente, felicitaciones a los creadores del contenido y de los Notebooks

创建者 Gerardo J P

Mar 25, 2020

A very good course to start learning Python and, of course, data analysis.

创建者 Yohanes

Jan 20, 2020

It's Good for people who wants to know python and data analysis for newbie

创建者 KUMAR B P

Mar 12, 2019

The course is unique in it's own way, simple in understanding and applying

创建者 Haley T

Jan 16, 2019

One of my favorite courses on Coursera. I have learned a lot about Python.

创建者 Ali S

Aug 26, 2024

That's absolutely awesome and effective. special thanks to Coursera team.

创建者 Carolina Z O

Aug 28, 2020

Es un curso completo, con el recurso del laboratorio de IBM es excelente.

创建者 Alejandro G

Apr 24, 2020

The topics are well explain to catch up in statistical terms and concepts

创建者 Geetha S

Mar 2, 2020

Well thought off module as well inputs and project work are well designed

创建者 RICARDO R A

Feb 17, 2020

Excelente curso, los laboratorios brindan una mejor comprensión del curso

创建者 Saad Z

Nov 7, 2019

Probably the best to teach data analysis using Python. Thank you so much.

创建者 Kalpa

Apr 18, 2019

Hands on and practical course, Lab exercises help to drive in the methods

创建者 Cláudia P d r

Mar 23, 2025

Curso muito bom!! Estou aprendendo muito com os cursos da IBM - COURSERA

创建者 BELGACEM G

Sep 15, 2023

Je suis vraiment trés heureux d'avoir terminé ces cours , merci beaucoup