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學生對 IBM 提供的 使用 Python 进行数据分析 的評價和反饋

4.7
14,243 個評分
2,110 條評論

課程概述

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....

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RP
2019年4月19日

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.

SC
2020年5月5日

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.

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2051 - 使用 Python 进行数据分析 的 2075 個評論(共 2,104 個)

創建者 ubaid m w

2018年10月22日

In lab there are many funtion , libiraries Which have been used first time with out any description , then I have to search for each and every funtion or lib which is way time consuming which make this course worst courses in my list.

創建者 jitao f

2019年5月2日

The content of this course is too basic. Though it provides enough knowledge to start a practice. No 0 to 1 but more like 0 to 0.1. And Forum support is terrible. Can't really answer my question( don't even think they have read it ).

創建者 Zackary M

2019年10月29日

The topics are great, but the content is pretty terrible. The questions are incorrectly formatted and hard to understand. It would be nice if someone reviewed these before they make them live and you have to pay for them.

創建者 Brahmrysti A B

2020年6月2日

A lot of mistakes here. Clearly rushed and not given the care and attention it needed. Some assignments REQUIRE you to go to the discussion board to figure out what the author intended and why your code isnt working.

創建者 Ashish D

2019年12月22日

Does the job of a good introduction.

Very limited and restrictive practice and assinments.

For a true learning experience one needs to do a lot of external research and work to show a measureable benifit.

創建者 Steve H

2020年5月19日

The content is good but there are a lot of mistakes and typos in the material. The peer review is extremely vulnerable to errors - only one person reviewed my assignment and gave me the wrong mark.

創建者 D W

2019年8月17日

Useful course but riddled with typos & inconsistent questions and answers. Needs a proper review by someone (probably not the people answering on the forums, who didn't seem especially clued up).

創建者 Aaron C

2020年7月15日

The videos really are not very engaging (relative to any other course that I have completed here on Coursera). The concepts are not explained very thoroughly. Thanks anyway guys.

創建者 Berkay T

2019年9月27日

Too much content, so less practice. This course doesn't teach anything that you can make use of in the long term. It only gives an idea on what you have to work on in the future.

創建者 Sheen D

2019年8月11日

This is by far the worst course in the specialization. So many mistakes in the lab session, including unclear instruction, or syntax is not uniform across each module, and etc.

創建者 Cláudia S B

2021年7月16日

The artificial voice used over the video is truly awful for learning. I enjoyed the jupyter notebooks where I actually could learn what was bla-bla-blaed in the videos

創建者 Michael M

2019年12月20日

The IBM Developer Skills Network (at labs.cognitiveclass.ai) is very slow and doesn't work most of the time.

It doesn't allow to finish the course properly.

創建者 Ismael S

2019年6月2日

Content is thrown to the student with too much information and videos of only 3-4 minutes. Too much to absorb and too little to practice properly

創建者 Archana B

2021年4月28日

Model Development and Model Evaluation & Refinement Concepts are not explained properly neither in Videos nor in Lab!!Really disappointing :(

創建者 Tarun S

2021年3月10日

Concepts of the algorithms are unclear. In the notebooks as well, it is not in a flow. Very confusingg for a beginner to learn from this.

創建者 Malcom L

2019年1月11日

more hands on, project based/game based learning. Mindlessly watching videos and regurgitating code in the labs can not be the only way.

創建者 Santanu B

2019年4月16日

Not a great course. Sometimes it is too fast and the explanations are very short. More hands on exercises would have been more helpful.

創建者 Rajesh W

2018年10月17日

There are plenty of mistakes in the videos and in the lab session as well. Hope you guys can clear out those.

創建者 Wayne W M

2019年10月2日

This was a very challenging course. Some concepts were difficult to grasp and required additional research

創建者 Mark F

2020年4月8日

Frustrating when the peer reviewer doesn't actually understand Python and deducts marks for correct code.

創建者 Hunter I

2020年4月17日

Leaned some, but not a whole lot of real-world application, I recommend people take Python courses more

創建者 Ashwin D

2020年4月29日

Not enough hands on problems, including variety and volume. Expected more from an IBM program.

創建者 Nathaniel S

2020年3月29日

Don't spend your money on IBM Data Science Cert. Course labs are full of bugs and not working.

創建者 Edwin S J

2019年5月25日

Suddenly introduced complex codes and statistical functions. Videos were way too fast.