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Applied Social Network Analysis in Python に戻る

ミシガン大学(University of Michigan) による Applied Social Network Analysis in Python の受講者のレビューおよびフィードバック

4.7
2,183件の評価
354件のレビュー

コースについて

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....

人気のレビュー

NK

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.

JL

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.

フィルター:

Applied Social Network Analysis in Python: 326 - 343 / 343 レビュー

by Vinit D

Jan 16, 2020

Tough course

by Avi R

Aug 03, 2019

Satisfactory

by Jean E K

May 18, 2018

good teacher

by TEJASWI S

Aug 02, 2019

Good course

by Andreas C

Dec 03, 2017

quite good

by Chethan S L

Oct 02, 2019

Excellent

by Xing W

Dec 04, 2017

Not bad

by shubham z

Jun 13, 2020

good

by Mallikarjuna R Y

May 05, 2020

good

by 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.

by 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 :)

by József V

May 05, 2018

Useful but weaker comparing to Pandas or Scikit courses.

by Sara C

May 17, 2018

i like the way that lecturer teach.

by Leon V

Oct 08, 2017

it was okay, 3.5 really

by DAWUN J

Apr 07, 2018

hm..

by Natasha D

Dec 05, 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.

by Moustafa A S

Aug 19, 2020

not usefull course, out dated materials and it doesn't work on new library, what's the use of it if it doesn't work anymore and noone uses it?

by sonam a

Dec 18, 2019

not interesting.