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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

1,761개의 평가
290개의 리뷰

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


May 03, 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 24, 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.

필터링 기준:

Applied Social Network Analysis in Python의 279개 리뷰 중 251~275

교육 기관: Siwei Y

Sep 21, 2017

老师讲解的非常好 , 逻辑清楚,条理明晰。建议编程作业稍微有点难度。所以扣掉一颗星。 希望越来越好。

교육 기관: Yang F

Sep 22, 2017

The first three weeks are very well planned.

교육 기관: Christian E

Mar 27, 2019

Very new on this topic and very interesting

교육 기관: David W

Oct 12, 2017

Challenging course and great instruction.

교육 기관: Brian R v K

Oct 23, 2017

Great fun, with practical application.

교육 기관: Cyrus N P

Jan 24, 2019

Well the subject was really hard.

교육 기관: Rupert

Jan 23, 2018

Good introduction into graphs!

교육 기관: Selvakumar

Jun 20, 2018

This is awesome course!

교육 기관: Nicolas B

Oct 06, 2017

Very good Course.

교육 기관: Arpit M

Dec 15, 2018

very good course

교육 기관: Raghunath P

Nov 11, 2018

Great Course!

교육 기관: Vinit D

Jan 16, 2020

Tough course

교육 기관: Avi R

Aug 03, 2019


교육 기관: Jean E K

May 18, 2018

good teacher

교육 기관: TEJASWI S

Aug 02, 2019

Good course

교육 기관: Andreas C

Dec 03, 2017

quite good

교육 기관: Chethan S L

Oct 02, 2019


교육 기관: Xing W

Dec 04, 2017

Not bad

교육 기관: Mallikarjuna R Y

May 05, 2020


교육 기관: Mark H

Feb 07, 2018

I liked the lecturer and the tempo of the lectures, but this course felt a little light compared to the others in the specialization. The quizes were also good. But for me the course was a bit off topic. Given that, the various skills I learned in the other courses did come together in the final programming assignment. As a stand alone course I would give it four stars, but it gets three because it's required for the data science specialization.

교육 기관: Siddharth S

Jun 14, 2018

The Course Deserves 5 Stars BUTThe fundamental flaw that felt absent in the last two courses of the specialisation was the in lecture Jupyter Notebook Demonstrations, it really helped the students feel in sync with the mentors.Please correct the same all the 5 courses of this specialisation deserve 5 starts :)

교육 기관: Philipp A R

Apr 07, 2020

I think that assignments 1-3 were too basic; often, you only had to return a simple function which outputs a specific network metric. Assignment 4 was a lot better, as it comprised the necessity to apply knowledge from previous courses. The instructor did a good job explaining the different concepts!

교육 기관: József V

May 05, 2018

Useful but weaker comparing to Pandas or Scikit courses.

교육 기관: Sara C

May 17, 2018

i like the way that lecturer teach.

교육 기관: Leon V

Oct 08, 2017

it was okay, 3.5 really