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学生对 IBM 提供的 使用 Python 进行数据分析 的评价和反馈

14,043 个评分
2,083 条评论


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....


May 5, 2020

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.

Apr 19, 2019

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.


76 - 使用 Python 进行数据分析 的 100 个评论(共 2,076 个)

创建者 Sadanand B

Feb 7, 2019

Seems like there are quite a few errors in the labs that confuse the heck out of a student. The labs need to be fixed else the material becomes useless.

创建者 Ravindra D

May 11, 2020

Course content does not give proper understanding of the different approaches. For the person who is not from mathematics background it is confusing.

创建者 Bhuvaneswari V

Mar 9, 2019

The statistics background needed for the course need to be better explained. or at least reference to related learning materials to be given

创建者 Russell K

Apr 26, 2020

Too many errors in the lab examples can be rather confusing.

Also, the Seaborn code was not working in IBM Watson Studio

创建者 Mariam H

May 2, 2020

Great course but some of the concepts are not explained very well. I got lost towards the end but overall i like it.

创建者 Andre L

Mar 10, 2019

Lot of information, but offered in a very choppy manner. Was hard to follow, will need to review many many times

创建者 Abdulaziz A

Apr 11, 2020

the course content is excellent but some Technical issues occurred in doing the lab exercises

创建者 Chau N N H

Jan 29, 2020

The lesson need more explanations on Polynomial Regression, Pipeline, Ridge Regression.

创建者 Fayja H

Jan 19, 2021

too much content all at once

创建者 Alex H

Oct 4, 2019

Begins relatively clear. The practice labs were coherent and straightforward.

Around Week 4, things started to get convoluted. Small things, things that you don't notice at first.

Week 5 was where it really started to fall apart. You could tell whoever made this course lost interest or just did not have the capacity to teach the information effectively.

A great example of the lack of understanding or knowledge of how Coursera works is something you can view yourself.

Week 6 is the Final Project

Week 7 is one statement about your certificate.

Usually in most courses, the final project will be in end of the final week. That week having multiple modules that you have to complete leading up to the final. It was worrying for me as I thought the approach to this was on accident, but it seems likely that it was just due to ignorance.

Just as well, the Final Project was botched, the software and questions were depreciated and even written wrong by the creator. And when you would upload your pictures in the end to show you had worked out the problem, one of the upload buttons was missing in lieu of the letter "Y"....

Y indeed. Y was the ending of this course so terrible? A little more investment in the people you are teaching would go a long way. Very disappointed.

创建者 Philip P

Jan 9, 2021

Course lacks thorough rigor or genuine assessment.

Labs are training on copy/paste and using the Shift+Enter command in the Jupyter notebook.

Assessments are multiple choice. No assessments on ability to write scripts to undertake data analysis to seek solutions.

创建者 Brandon S

Jan 7, 2021

Again, the use of the IBM cloud is a useless buffering of site traffic for your own products and does not provide anything for the course. Little to no 'challenge' questions that push the student to go beyond the hand held procedure of the labs.

创建者 Elvijs M

Apr 18, 2020

The course makes you aware of some Data Analysis techniques, but you learn very little. The explanations are very superficial. And since nearly all the code is are already there, you are not forced to think about the concepts and methods.

创建者 Utkarsh S

Jun 25, 2020

The course was quite good until Week 3 but after that it was poorly structured. A lot of concepts were randomly introduced without proper explanation in Week 4 and Week 5, thereby killing the fun of learning.

创建者 Ibrahim A

Apr 27, 2020

This course ranks the least of the wonderful courses I have taken with coursera. There is definitely room for improvement in the delivery of materials.

创建者 Muzamal A

Apr 22, 2020

I'll be honest this course for a beginner is difficult and incomprehensible as thereare many new things introduced which are not explained properly

创建者 Sharvinee

Nov 23, 2020


创建者 Benjamin J

Dec 1, 2018

many mistakes throughout

创建者 Hakki K

Jul 9, 2020


I completed entire program and received the Professional Certificate. On the Coursera link of my certificate "3 weeks of study, 2-3 hours/week average per course" is written. This information is not correct at all, it takes approximately 3 times of that time on average! I informed Coursera about it but no correction was made. It should be corrected with "it takes approximately 19 hours study per course" or "Approx. 10 months to complete Suggested 4 hours/week for the Professional Certificate".

Here is the approximate duration for each course can be found one by one clicking the webpages of the courses in the professional certificate webpage: (*)

Course 1: approximately 9 hours to complete

Course 2: approximately 16 hours to complete

Course 3: approximately 9 hours to complete

Course 4: approximately 22 hours to complete

Course 5: approximately 14 hours to complete

Course 6: approximately 16 hours to complete

Course 7: approximately 16 hours to complete

Course 8: approximately 20 hours to complete

Course 9: approximately 47 hours to complete

This makes in total approximately 169 hours to complete the Professional Certificate. As there are 9 courses, each course takes approximately 19 hours (=169/9) to complete.


创建者 Vera

Apr 7, 2021

The general course content was okay. Unfortunately I didn't learn too much about Python and Data Analysis for Data Scientists. This was due to the following reasons:

1) a lot of interaction with not working IBM infrastructure. It took me around 3x as much time to get required things working on IBM cloud and IBM Watson compared to the time spent for actual assessments. It is annoying if it's getting that obvious that IBM wants to use the course to promote own products. This is sad as we all already pay for the course...

2) There occurred quiet some arrows in the labs which even after months (according to the discussion) have to been corrected.

3) The amount of hands-on training in the notebooks/labs was really small. It was not a lot one had to program on their own and the parts which had to be programmed were only an exact copy of what was already done before. Even the final assessment did not really contain a real task.

4) Many concepts weren't explained in depth. The explanations just stayed very superficial. Some concepts like fit()/fit_transform() which appeared in the labs weren't explained at all in the videos or in the labs. This led to a lot of confusion as could be seen in the discussion threads.

As we all pay for this course please increase the amount of actually explaining concepts in depth and the amount of real in depth hands-on training and reduce the parts on IBM Watson and other such stuff. Thanks a lot!

创建者 Jennifer R

Mar 31, 2020

The topic is very interesting, but the execution was poor. Code and numbers were just being read at me, instead of focusing the recorded lectures on teaching concepts and troubleshooting, and leave the code to be read by myself in the labs. Also, the quizzes along the way were nearly useless: only two questions, a "pass with at least 50%", and the questions asked were very superficial. This is the most poorly executed course I have taken on Coursera so far.

创建者 Nizami I

Oct 6, 2019

The course structure and videos are nice, but THERE ARE SO MANY ERRORS in the videos. I spent so much time to google and fix these errors. It is really terrible and I dont understand how people gave the high grade. I stopped watching videos after Week 3, because I fed up correcting their errors. Although people have mentioned it long time ago, but nothing has changed. Really shame on Coursera and IBM that have such quality!!!

创建者 Matthew A

Apr 13, 2021

During the 4th week of the course, lots of important information and explanations are over summarized and in some cases skipped over. Learning tools outside of what is provided in the course or a decent understanding statistics is required in order to be successful in this course.

创建者 Thamarak

Aug 22, 2020

This course is too hard. This should be go on more slowly and explain more about meaning of each value described. The course is not for beginner and not for a person who doesn't have enough statistics background.

创建者 Abhijit R

Sep 6, 2019

Course content is very poor. Not clearly explaining each & every thing in each slide. Disgusting