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Applied Social Network Analysis in Python(으)로 돌아가기

미시건 대학교의 Applied Social Network Analysis in Python 학습자 리뷰 및 피드백

2,570개의 평가

강좌 소개

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....

최상위 리뷰


2019년 5월 2일

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.


2018년 9월 23일

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.

필터링 기준:

Applied Social Network Analysis in Python의 427개 리뷰 중 401~425

교육 기관: Nicolas B

2017년 10월 6일

Very good Course.


2020년 10월 16일

nicely explained

교육 기관: arpit m

2018년 12월 15일

very good course

교육 기관: Raghunath P

2018년 11월 10일

Great Course!

교육 기관: Vinit D

2020년 1월 16일

Tough course

교육 기관: Avi R

2019년 8월 3일


교육 기관: Jean E K

2018년 5월 18일

good teacher

교육 기관: TEJASWI S

2019년 8월 2일

Good course

교육 기관: Andreas C

2017년 12월 2일

quite good

교육 기관: Chethan S L

2019년 10월 2일


교육 기관: Xing W

2017년 12월 3일

Not bad

교육 기관: shubham z

2020년 6월 13일


교육 기관: Mallikarjuna R Y

2020년 5월 5일


교육 기관: V B

2020년 12월 30일


교육 기관: Alexandra C

2021년 2월 28일

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

교육 기관: Daniel B

2020년 12월 18일

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 On top of that, the course uses the outdated networkx 1.11, while 2.0 has been out for over three years.

교육 기관: Jeremy .

2021년 1월 1일

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

교육 기관: Jenny z

2020년 12월 1일

better if TA could prepare projects with updated versions of libraries

교육 기관: József V

2018년 5월 4일

Useful but weaker comparing to Pandas or Scikit courses.

교육 기관: Sara C

2018년 5월 16일

i like the way that lecturer teach.

교육 기관: Leon V

2017년 10월 8일

it was okay, 3.5 really

교육 기관: DW J

2018년 4월 6일


교육 기관: Afreen F

2021년 2월 7일

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.

교육 기관: MENAGE

2021년 2월 22일

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

교육 기관: Natasha D

2019년 12월 5일

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.