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學生對 IBM 技能网络 提供的 使用 Python 进行数据分析 的評價和反饋

15,264 個評分


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....




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.



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.


2201 - 使用 Python 进行数据分析 的 2225 個評論(共 2,309 個)

創建者 Gerhard E


Copy of videos, not a fan of tools used in labs

創建者 Aditya D J


Nice, but I can't try IBM Cloud Trial for free

創建者 Yasmin A


Un cours riche et adéquat pour les débutants

創建者 Hiro H


Very nice course. It gives you what you need

創建者 Brian S


Notebooks are sloppy, with typos and errors

創建者 Anjali


I am not able to download my certificate.

創建者 Fariha M


The course didn't seem challenging to me.

創建者 Sachin L


More examples and detailed explanation

創建者 Nilanjana


More examples and code examples needed

創建者 Hamed A


The course needs a final assignment!

創建者 piyush d


exercises could have been better.

創建者 Jyoti M


I felt it was too fast to grasp.

創建者 Baptiste M


Final assignment is quite messy

創建者 Murat A


could not access the labs.

創建者 Yuanyuan J


Not clear on the last part

創建者 Ahmad H


This course is very tough

創建者 conan s


Lots of technical issues

創建者 David V R


Exams should be harder

創建者 Riddhima S


la lala la la laa aaa

創建者 Daniel S


Not easy to follow.

創建者 Allan G G


Muy poco practico



très bon cours

創建者 Vidya R


Very Math!

創建者 James H


Definitely not one of my favorite courses in the Data Science Certificate series. There were times I was ready to give up the pursuit of the certificate altogether during this class... There should have been a prerequisite for this course of the statistical tools and methods that would be covered in here... Sure I could program these things after this class, but i still dont understand why I would choose to use one over another? This is one of those classes where you walk away feeling more confused than when you went in... Also there were a lot of mistakes, typos, and obsolete things in the labwork - some reported and acknowledged months ago, but still not fixed in the lab (video I can understand, but not the labs)

創建者 Ruben W


The content is good, but if you are not familiar with Python, I wouldn´t recommend this course. There are a lot of typos in the video. The code contains a lot of errors where you have to find a solution. So, you are forced to debug their code often.

But if you are only interested in the course certificate, you could quickly go through the videos and quizzes, without any problems. It's easy to pass because the questions are like: What is the result of print("Hello world"). So no real challenges at all.

Please, try to fix the typos. Sometimes it was very embarrassing. Example (Week 3) instead of

"from sklearn.metrics ..." the video comes up with "from sklearn.metrixs ..."