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学生对 密歇根大学 提供的 Applied Social Network Analysis in Python 的评价和反馈

2,573 个评分


This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python....



May 2, 2019

This course is a excellent introduction to social network analysis. Learnt a lot about how social network works. Anyone learning Machine Learning and AI should definitely take this course. It's good.


Sep 23, 2018

It was an easy introductory course that is well structured and well explained. Took me roughly a weekend and I thoroughly enjoyed it. Hope the professor follows up with more advanced material.


401 - Applied Social Network Analysis in Python 的 425 个评论(共 427 个)

创建者 Nicolas B

Oct 6, 2017

Very good Course.


Oct 16, 2020

nicely explained

创建者 arpit m

Dec 15, 2018

very good course

创建者 Raghunath P

Nov 10, 2018

Great Course!

创建者 Vinit D

Jan 16, 2020

Tough course

创建者 Avi R

Aug 3, 2019


创建者 Jean E K

May 18, 2018

good teacher


Aug 2, 2019

Good course

创建者 Andreas C

Dec 2, 2017

quite good

创建者 Chethan S L

Oct 2, 2019


创建者 Xing W

Dec 3, 2017

Not bad

创建者 shubham z

Jun 13, 2020


创建者 Mallikarjuna R Y

May 5, 2020


创建者 V B

Dec 30, 2020


创建者 Alexandra C

Feb 28, 2021

Videos are very distracting as there are many cutscene from the text to the instructor's face which is very disrupting for the flow of the lecture. Maybe overlaying his face on a small window on the corner will be better

创建者 Daniel B

Dec 18, 2020

This course feels more like an API summary of networkx rather than a real course on social network analysis. On top of that, the course uses the outdated networkx 1.11, while 2.0 has been out for over three years.

创建者 Jeremy .

Jan 1, 2021

Some of the assignment organization could have been better, but otherwise the information was rock solid!

创建者 Jenny z

Dec 1, 2020

better if TA could prepare projects with updated versions of libraries

创建者 József V

May 4, 2018

Useful but weaker comparing to Pandas or Scikit courses.

创建者 Sara C

May 16, 2018

i like the way that lecturer teach.

创建者 Leon V

Oct 8, 2017

it was okay, 3.5 really

创建者 DW J

Apr 6, 2018


创建者 Afreen F

Feb 7, 2021

Lecture Videos are good but it seems 0 efforts were put in the assessments. The auto-grader is especially a pain and you end up spending LOT of time around trivial issues with the auto-grader.


Feb 22, 2021

Aimerais avoir plus de temps et de conseils pour bien réussir..

创建者 Natasha D

Dec 5, 2019

The lectures and first three assignment are extremely superficial. Mostly they throw a bunch of definitions of metrics at you, give you some one-liners that will calculate specific metrics, then ask you to spit back those one liners (essentially no discussion of applications, etc). Then the fourth and final assignment is an interesting application of what you've learned but the grader is a NIGHTMARE. It is super buggy and your true task is to learn how the grader works, not how to write code and apply what you've learned about data science. I would not recommend this course unless you need it to finish the specialization.