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

4.7
19,613 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

BK

May 8, 2021

I love the practicality of this course. It's not just learning theories but you actually follow along. You only need a good computer and you learn serious staff taught in the best way possible.

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:

2576 - Data Analysis with Python 的 2600 个评论(共 3,115 个)

创建者 Akash M

Jan 26, 2020

nice and balanced course i like the assignments in this course a lot to learn here

创建者 Dave P

Apr 14, 2024

Great course and powerful learning -I look forward to the application now! Thanks

创建者 Edinson R S A

Mar 5, 2020

Good course, more explanation of the interpretations of the results will be great

创建者 Richard P

Oct 3, 2024

This is a must have course that every aspiring analyst should consider enrolling

创建者 Prentice D T

Dec 29, 2019

Better explanation could have been given for beginners, otherwise, it was good.

创建者 Diego S

Sep 22, 2019

I think needs to append more exercise. Too much content for a few of exercises

创建者 Ghassan H

Jun 30, 2025

Good and practical skills for people starting out on the Data Science journey

创建者 Alex U

May 27, 2021

Good course, But it would be better to insert more additional practise tasks.

创建者 Mohit

Nov 24, 2020

Teacher teaches in a well mannered its quiz and lab session help me a lot .

创建者 Steven M

Jan 26, 2020

Una muy buena opción si se quiere empezar con el análisis de datos con python

创建者 Abhishek k

Mar 31, 2024

Nice and Excellent content of data analysis which will be very beneficial .

创建者 Shubhodeep M

Oct 22, 2021

It is a great first step towards learning data analysis. Highly recommended.

创建者 Mohammad Q

Aug 21, 2019

Great but it has lots of information and require simple statical background

创建者 Harsh B

May 31, 2025

videos could have been a bit more informative otherwise labs were very gud

创建者 Saqlain H S

Oct 13, 2019

This course is very useful if you want to learn the field of data science.

创建者 Adam C

Feb 14, 2021

Good course, Get's a bit tricky when it starts talking about regression.

创建者 Coco

Jan 14, 2020

Quizs could be more practical. The part of explaining models is amazing!

创建者 Manuel O

Aug 21, 2019

Learning may be more beneficially if we actually wrote most of the code.

创建者 Kisha B

Jun 28, 2019

I took this course out of sequence, but it has been the best one so far!

创建者 ZHANG B

Apr 22, 2019

The content in the lab is great! However, video courses are not so good.

创建者 Raphael I

Apr 27, 2021

great course,

exam handling not learner friendly & not very challenging

创建者 shiva m a

Aug 3, 2020

Awesome introduction to data analysis with python. Loved it absolutely!

创建者 Moaz M

Oct 10, 2019

some topics have not been covered well like piplines , cross validation

创建者 David O

Jul 26, 2020

The materials are well-organized, but there are many typos throughout.

创建者 Angeliki M

Dec 2, 2019

A really good course. Probably the best so far in the IBM Certificate.