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學生對 密歇根大学 提供的 Introduction to Data Science in Python 的評價和反饋

4.5
25,642 個評分
5,715 條評論

課程概述

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....

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PK

2020年5月9日

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

YY

2021年9月28日

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.

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5076 - Introduction to Data Science in Python 的 5100 個評論(共 5,665 個)

創建者 KULKARNI G

2022年2月8日

This is good course but you should really have good knowledge of python before doing this course. even if this is introduction course still python basic knowledge is needed

創建者 Navaneet K A

2019年5月29日

The accent of the instructor was too fast, which made the content incomprehensible. The course unfolded very rapidly, giving little room for beginners to understand on spot

創建者 Anastasiia P

2020年7月18日

I think, that the structure of the lectures made it difficult to concentrate on the material, but the assignments were very good and I have learned a lot while doing them.

創建者 Apurva

2018年1月17日

First three modules are great but in the last module (Week 4) Prof Brooks is rushing through many topics. Probably consider having lesser content with proper explanation.

創建者 Estapraq M K

2018年1月7日

The instructor doesn't explain that much, he could do better than that. It is an independent study. The only thing I enjoyed was the links and the articles, that was all.

創建者 Micah D

2018年5月2日

Course has a great amount of information that is wonderful, but the instructional videos are less and less helpful as time goes on, and the autograder is the devil.

創建者 Vishwakarthik R

2017年7月29日

The course content was good but the assignments were way too tough.The assignments should have been a bit easier because i lost interest due to the tough questions.

創建者 Jan K

2017年7月15日

The programming assignments were a little frustrating.

I feel a little more time should be spent on the theory behind pandas and how the library works conceptually.

創建者 Hong_Linshuo

2019年7月9日

I think the assignments waste too much of my time since I have no problems using proper programming skills, but have lots of problems catering to the auto grader.

創建者 Taras P

2016年12月10日

Top free course about Data Science. But I think lectures must be more detailed and related to assignments. And assignments could be less ambiguous and more clear.

創建者 Manuela D

2018年1月3日

Some exercises of the assignments where ways to difficult compared to what learned during lectures: much more details should be provided about data manipulation

創建者 PRACHUR G

2020年4月27日

the course is really good but there are issues with autograder. Though they are addressed in forums you'll have to go through them and hence wasting your time.

創建者 Joshua C

2018年1月24日

You'll spend more time struggling with the jupyter notebook (assignment platform) than actually writing or learning code. The lectures are really good, though.

創建者 Yan X

2019年11月4日

Great content. But some assignment questions are not that clear and might cost you more time than its worth. And feedback from mentor is not that responsive.

創建者 Abhijit G

2018年4月27日

The course is well designed and assignments are complex. What I did not like about this course is that the assignments are not well explained with examples.

創建者 Narayan S

2020年8月17日

The main problem is with the auto grader. There are too many issues making it cumbersome to get the assignment submission right in one go. Please fix this.

創建者 Parth M

2020年7月12日

Had to learn most of it by myself. Got discouraging at a certain point. Should have informed about the prerequisites.

Learn Numpy, Pandas before enrolling.

創建者 Ryan T

2020年5月18日

Some parts were quickly rushed through and poorly explained. However, they did explain the bare bones of pandas, which was the main reason for this course.

創建者 Pengyue S

2018年7月1日

There is one critical technical problem lying in the assignment three and already caused hundreds of students' grade blank in the forum, including myself.

創建者 Nehal c

2019年7月3日

As a beginner I found it a bit of a brisk over the topic. There was a lack of basic questions. But in the end I was coping up and then the course ended.

創建者 KUSHAL B

2020年7月14日

too fast in explaining it was bit difficult to keep up with the explanation,small code example were taught but assignments questions was too difficult

創建者 Aram M

2018年5月25日

Great course material, but the autograder system was frustrating to work with for assignments, and often made me less motivated to work on the course.

創建者 Himansu A

2019年1月16日

The course is okay for beginners as it is having only few lecturers for basics. Coursera experience was good. Overall i am satisfied with the course.

創建者 Yaseen H

2018年9月24日

The assignments are not even close what is being taught. We are taking this course so we get everything in one place. Curriculum has to be improved

創建者 Alvaro B F

2021年8月30日

I​ think the lecture about grouping could be improved with more practical examples, I had to search for external sources to understand the concept.