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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
15,111 個評分
2,276 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....

熱門審閱

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.

篩選依據:

1676 - 使用 Python 进行数据分析 的 1700 個評論(共 2,282 個)

創建者 Tracey C

2021年2月11日

I liked the structure and pace of this course. The videos and exercises were helpful and the final project was a very good measure of what we had done in the course. I took off a star because there were more typos in this course than some of the others.

創建者 Lakshmi h

2020年7月9日

There should be an Handicap assistance in the course as some of the visually impaired people are finding it difficult to read the assignment codes with their screen reader nvda.

The assignment notebooks code settings need to be modified to support this.

創建者 Dean E B

2021年5月28日

Covers lots of materials. Lab is at end of each week, but I did better following along with coding during each lesson, A good framework, but with a lot of jumps and not much depth. With additional studying from other sources, I got a lot of knowledge.

創建者 Whale M J

2020年9月2日

A lot of concepts are packed into this little course. The course materials are a bit too concise for the concepts to be elaborated properly, so I need to search a lot extra online for concepts behind. But in general, they can be used a starting point.

創建者 Antas J

2020年1月8日

the course was great and informative, however the pace and information in this course is not sufficient for a person who is new to the python libraries and analytical features, if i may add MSE and R^2 and plots are still not so much understood by me.

創建者 Aylin G

2020年1月2日

Some questions in the peer-graded assignment are not clear and answer box of some questions are not visible so I could not get any point from them. You should better check the contents of the tutorial and make sure that there is no technical issues.

創建者 giuseppe t

2022年3月31日

​It is a well structured and quite valuable course; it could have been a masterpiece, if it had provided more connections, explanations, insights, in other words programming background related to all those different topics touched over the weeks.

創建者 Monalisa p

2019年11月4日

This Course is very helpful for the beginners. This course is very detailed, and well explained. You will go through all the important things required for data analysis. This course's Lab is very strong, I must recommend you to do this course.

創建者 ERNESTO C O C

2022年2月10日

Bom conteúdo na abordagem das principais funções para análise de dados, porém, carente de fundamentação teórica em relação às análises. Por não haver pre requisito, os fundamento poderiam ter sido abordados, ainda que de forma superficial.

創建者 Sachin G

2020年5月4日

Very informative course... very well designed... a bit fast-paced but concise and clear

it's just that if the final project could have been little more challenging so that there are more opportunities to apply what we learnt in the course.

創建者 Terry G

2020年5月1日

Great course. I felt like I can run my own models and test them now. There were some strange errors throughout the notebook that were raised in the forums but not addressed in the documents. Aside from that, it was pretty good for a MOOC.

創建者 Adarsh K P

2019年9月26日

ton of new stuff to learn from.... super informative course...this course will introduce you to a lot of useful and important stuff and the best part is that each topic is explained first then comes the coding part which is just awesome.

創建者 Chris A B

2019年9月15日

This was a challenging course that covered a lot of items. I believe I need more practice in these items (Linear Regression, Polynomials, Ridge, Fit, Predict, etc.) in order to have a much better understanding of the course materials.

創建者 Guilherme P d C

2019年4月7日

Model Development and Model Evaluation content requires more intuitive examples, maybe adding some flowchart to explain the reasons of every step in Modeling and Evaluation. I am making this suggestion to make the course even better.

創建者 Joe M

2020年3月27日

Interesting class. Clearly designed to cover a lot of ground but not always in the detail some may like. Emphasizes showing some basic analytic work flows, but does not always explain how or why of a particular step in the workflow.

創建者 Logan W

2019年11月7日

This was a very comprehensive course, but it could definitely use some revising on the labs that caused output issues. Additionally, some of the peer-graded material couldn't be uploaded due to syntax. Other than that, very helpful!

創建者 João L F C

2020年4月16日

It was a good introductory and pocket course for Data Analysis with Python to me. The concepts were given pretty much straight forward, and the assignement didn't diverged much from what had been already seen throughout the course.

創建者 Jhon P

2021年6月4日

At the end, the final project link was wrong and nobody from coursera or ibm give me an answer. Fortunately, course partners share the right notebook and thus, I could finish my course. The topics are very well for this course.

創建者 Michael g

2022年3月4日

t​his was one of the better courses in the 10 course package. a bit more focused and less slapped together than the previous courses. also the lab load and have no glittches, aleast for me, unlike other courses. overall good

創建者 Olatoye D S

2022年5月27日

This is course is a great way of understanding Data Analysis, model training and evaluation, as well as a further indepth understanding of Exploratory Data Analysis, using Python. It was a great time learning through it.

創建者 Enrico G

2022年4月9日

Very nice and practical course. It gives you the tools to perform a regression analysis on a dataset. Perhaps, I might have focus a little more on the mathematical theory behind some method, like correlation, p-value...

創建者 Rajan G

2020年6月16日

This course helped me a lot in solving my basics about data cleaning, Visualization, Techniques for getting better result and most important how we can judge whether a model is good or not. Thanks for this great course.

創建者 asher b

2018年11月12日

this course finally gets to some key Python functions for data analysis. Some of this may be difficult without a basic stats background. Only knock is there are MULTIPLE typos in the slides and labs. Needs to get fixed.

創建者 Miranda C

2020年7月23日

This course went fairly well, I just hope that the information will be repeated in the next course in the certificate program (IBM Data Science certificate) as I don't feel like the information has really sunk in . . .

創建者 Ankit S C

2020年1月15日

The Model Training and Evaluation weeks could have been more elaborate. Instead of just telling to do something, it would've been better to explain why we are doing it and how is it working internally, at a high level.