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学生对 密歇根大学 提供的 Introduction to Data Science in Python 的评价和反馈

25,761 个评分
5,734 条评论


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....



Sep 28, 2021

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.


May 9, 2020

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


5226 - Introduction to Data Science in Python 的 5250 个评论(共 5,685 个)

创建者 Oswaldo C

Jun 29, 2020

You have to make a lot of research on pandas documentation to do the assigments

创建者 Mr Q

Jun 27, 2020

Typical school concept. Lessons level 1, quizzes level 3, assignments level 50.

创建者 Sid J

Aug 18, 2019

Bit faster than expected, should have disclaimer to learn pandas basics first.

创建者 ti t h

Oct 27, 2018

Auto grader needs to give more feedback. Too many things to learn by yourself

创建者 Anil K N

Jun 22, 2019

There should have been more quizzing assignments rather than the coding ones


Jun 29, 2020

in starting course is good but at the end it will become boring and lengthy

创建者 Thiago J M M

Apr 21, 2020

Os exercícios estão em nível absurdamente complicados emm relação as aulas!

创建者 Thomas G

Sep 25, 2019

It's really informative, but there is too little instruction in my opinion.

创建者 Jens R

Nov 28, 2018

In my point of view, the lectures and the assignments are not well matched.

创建者 Jai L 1

Apr 13, 2020

Tutotial videos of jupyter are very fast and need much time to understand.

创建者 Srikanth G

Nov 10, 2019

It would be nice if we have more explanation for questions in Assignments.

创建者 Shiva K

Jul 23, 2020

Very fast explanation and coding is also very fast and hard to understand

创建者 Wong L C

Nov 14, 2018

Overall is good but the contents seem to be a bit difficult and too rush.

创建者 Premjith B

Nov 21, 2016

The assignments were good but it was a lot of time spent with the grader.

创建者 Eric L

Mar 20, 2021

submitting the assignments with the autograding is extremely frustrating

创建者 Thi T H N

Aug 22, 2019

The course is not well organised. However, the projects are interesting.

创建者 Ryan S

Nov 22, 2017

homework format of outputting values from functions needs to be improved

创建者 Abhishek P

May 10, 2020

Course way too easy and has quite less information/knowledge to offer.

创建者 Shivam P

Jun 26, 2020

The assignment is too difficult compared to what they teach in course

创建者 tushar s

Jun 20, 2020

Not providing in-depth knowledge of functions through video lectures.

创建者 Chirag S

May 18, 2020

Expected a detailed explanation instead got a very brief explanation.

创建者 Christopher C

Mar 30, 2020

Only a few resources. Each Jupyter Notebook lack context and comments

创建者 Stefani N

May 29, 2018

I think the assigments are a bit difficult for an introduction course

创建者 Jaepil L

Mar 26, 2018

nice intro, but requires background knowledge and self-study,,,,a lot

创建者 Thanasis M

Aug 26, 2017

Very handy exercises, but the lesson lacked in examples and guidance.