Chevron Left
Вернуться к Applied Social Network Analysis in Python

Отзывы учащихся о курсе Applied Social Network Analysis in Python от партнера Мичиганский университет

Оценки: 2,558
Рецензии: 432

О курсе

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....

Лучшие рецензии


2 мая 2019 г.

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.


23 сент. 2018 г.

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.

Фильтр по:

151–175 из 425 отзывов о курсе Applied Social Network Analysis in Python

автор: Thaweedet

15 авг. 2018 г.

Great, You will to learn how to develop feature for social network data

автор: Mischa L

6 янв. 2018 г.

Great course. Very good homework assignments, but somewhat on easy side

автор: Rui

11 окт. 2017 г.

very good introductory course for social network analysis using Python.

автор: Diego F G L

30 мар. 2021 г.

Great course and and great contents. I really enjoyed the assignments.

автор: Dirisala S

22 июля 2019 г.

The have lot of stuff to learn. It will definitely enhance your skill.

автор: Dibyendu C

19 окт. 2018 г.

Well structured and quality lecture content with excellent assignments

автор: Nikhil N

18 июля 2021 г.

W​onderful course with very detailed explanations!!! Simply wonderful

автор: Liran Y

20 мая 2018 г.

Interesting and fun. Daniel's lecturing style is clear and enjoyable.

автор: Namrata T

24 мар. 2022 г.

Terrific Course. Learned a lot in graph theory and network analysis.

автор: Chiau H L

4 апр. 2019 г.

Awesome course!!! Helped me a lot to get started with graph analysis

автор: Keqi L

14 апр. 2019 г.

Interesting slides and knowledge. e.g. Page rank is super cool!!!!

автор: Kai H

8 нояб. 2018 г.

Good course, may be better if offer more practice and application.

автор: Tatek E

23 мар. 2020 г.

Excellent presentation, exercise and reading materials. Thank you

автор: wenzhu z

22 февр. 2018 г.

very clear logic, and will always wrap up at the end of the class

автор: 杨志陶

17 мая 2020 г.

A practical way to learn social network analysis. Great course!

автор: Renzo B

23 сент. 2019 г.

I learned a lot of things that I can apply to my line of work.

автор: Charles L

4 февр. 2019 г.

A completely new area for me, and a really fascinating course.

автор: Yee F

1 июля 2021 г.

Course is much easier to understand that applied text mining.

автор: Haris P D

31 янв. 2020 г.

One of the most awesome course that I have taken on Coursera!

автор: Wai Y P S

22 июня 2021 г.

Thanks you so much University of Michigan for Great course

автор: Marco Z

22 апр. 2020 г.

Very interesting , a new point of view for future analysis!

автор: Israel D D G

22 авг. 2020 г.

Excellent course, good technical and teoretical knowledge.

автор: LEE D D

5 нояб. 2017 г.

Excellent! It was one of the great assignments I ever had!

автор: Manuel T

30 янв. 2018 г.

good stuff. Assignments are a little bit too easy though.

автор: Jiahui B

28 нояб. 2017 г.

Very useful course. It helps me finish my course project.