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學生對 密歇根大学 提供的 Applied Social Network Analysis in Python 的評價和反饋

4.7
2,202 個評分
358 條評論

課程概述

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....

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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.

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51 - Applied Social Network Analysis in Python 的 75 個評論(共 347 個)

創建者 Yaron K

Sep 21, 2017

Excellent course. Lecturer clearly explains network analysis terms and algorithms with examples, and then shows how they are implemented by the Python networkX library. The assignments exercise their use.

創建者 João R W S

Oct 07, 2017

Very good course! I've learned a lot both in theory and practical aspects. The final assignment worth to put all together with the skills learned in the other 4 courses of the specialization. Great job!

創建者 Nitin K

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.

創建者 Christos G

Sep 18, 2017

Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.

創建者 dan s

Feb 25, 2018

I loved this course. It was well taught and had excellent problem sets and quizzes to internalize the learning. The material is very relevant to the market today. I highly recommend it.

創建者 Brian L

Apr 18, 2018

Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.

創建者 Spencer R

Mar 28, 2020

Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.

創建者 Luiz S

Aug 23, 2020

Basic yet informative course. The videos are well paced and the presenter is instructive. The exercises are well made, putting more enphasis on what was learned in the videos.

創建者 Nick P

Oct 08, 2017

Interesting material and easy to follow. Assignments and quizzes were sufficiently challenging, but not too difficult that I spent entire weekends troubleshooting my code.

創建者 Korkrid A

Sep 27, 2017

It's rare to find an amazing course in network analysis online, and I'm very glad to have taken this course and learn the art of network analysis for research purposes.

創建者 Servio P

Nov 18, 2017

This course contains many important concepts of Graph Theory and Network Analysis. The explanation is clear and neat. Also, the assignments are fun and comprehensible.

創建者 Saurabh S

Feb 19, 2018

Very comprehensive course for introduction of social network analysis. Best part is every concept is covered in detail and how to implement using networkx library.

創建者 Nussaibah B R S

Jun 02, 2019

I found it hard sometimes to understand the concepts but this gave me quite an introduction on social network analysis and encouraged me to learn more about them.

創建者 Jorge A S

Feb 27, 2018

Great explanations. The instructor is awesome and has good visual material. In-video quizzes keep you engaged during the lecture. I am very happy with the course.

創建者 谢仑辰

Mar 23, 2018

I really appreciate that you offer me such a great specialization of courses.Since I've finished the final course eventually, I should offer my gratitude to you.

創建者 Fabrice L

Nov 23, 2017

Very good class.

The lecturer is amazing!! The quizzes help you understand the concepts. The assignments are a little basic though.

Overall you learn a great deal.

創建者 Punam P

Apr 25, 2020

Very nice platform to learn & enhance skill. Thanks to Prof. and team.. Also thanks to university and coursera platform for providing such a big platform to us.

創建者 Morgan S

Jul 23, 2020

Great introductory course to graph theory! Dr. Romero is one of the most engaging professors that I've had, both in-person and online. The assignments are fun.

創建者 Jiaqi d

Dec 15, 2019

Really helpful. Get a basic idea of the social network and how to use python to analyze it. Will definitely dig deeper and see how it could relate to my work .

創建者 Avulapati N

Jul 03, 2020

A nice short course on Networks. This was one of the best courses I've taken on Coursera.

The course content, instructor and assignments are all amazing.

創建者 Piyush V

Jan 29, 2020

All over the course is very relevant to what is a need in industry. Very nice video lectures, to the point and crisp. Material is quite informative too.

創建者 M J

Jun 04, 2018

An excellent course which is well planned and executed! If you're following the specialization, it's a welcome relief after the text analysis course.

創建者 Lutz H

Jul 19, 2019

Great course! Really well explained with intuitive examples and great illustrations. At the end there is an interesting but challenging assignment.

創建者 Devon H

May 05, 2018

Great lecturer, comprehensive material and unlike other courses in this specialisation, actually prepares you well for the assignments and quizzes.

創建者 Atilio T

Mar 22, 2020

Excellent course. The lecturer explains in a simple way to understand, and exercise are interested to the analysis of social network using python.