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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

4.6
1,462 個評分
237 個審閱

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....

熱門審閱

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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101 - Applied Social Network Analysis in Python 的 125 個評論(共 229 個)

創建者 Víctor L

Mar 23, 2018

Excellent Course, very interesting, no idea that so many tools existed for network study and analysis. Excellent job both from the professor Daniel, and from Coursera/University of Michigan State. The QUIZES were very challenging, sometimes more than the Assignments. I'm really satisfied.

創建者 Ayush R

Aug 05, 2018

Better Explanation, Not too hard to solve .

創建者 Manuel A

Aug 22, 2018

Very challenging and comprehensive course, also directly applicable to machine learning problems, as an example, the last assignment applies network knowledge to extract features and exploit them in predictive modelling problems

創建者 Jan Z

Sep 07, 2018

Great course. Thank you!

創建者 Noureddine B

Sep 18, 2018

Excellent course.

創建者 Konstantinos M

Sep 13, 2018

Very interesting topic and well-made lessons.

創建者 Yusuf E

Sep 24, 2018

Coming into this course, I didn't expect much but I was pleasantly surprised by the quality of the material. The quizzes were especially designed well and the final assignment was really challenging and instructive. I wish there was more of predictive modeling using network features but the rest of the course easily makes up for that.

創建者 Jingting L

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.

創建者 Manuel V

Oct 16, 2018

in my opinon, the best of the specialization. thank you

創建者 Dibyendu C

Oct 19, 2018

Well structured and quality lecture content with excellent assignments

創建者 Sylvain D

Oct 19, 2018

Great course

創建者 Leonid I

Oct 18, 2018

Great course! Only one note: the online notebooks use an old version of networkx (v1.11), which is incompatible with the newer v2.2. Therefore, some trickery is required to read pickled networks locally...

創建者 Mohammad H

Oct 26, 2018

the course will teach basic of SNA so clear

創建者 Мирзабекян А В

Aug 09, 2018

One of the most interesting and challenging courses in specialization, in my opinion.

創建者 Thaweedet

Aug 16, 2018

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

創建者 Tam

Aug 18, 2018

Wow

創建者 Kai H

Nov 08, 2018

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

創建者 Armand L

Oct 28, 2018

Hard but instructive course !

創建者 Niranjan H

Nov 14, 2018

As a course by itself or as part of the specialization, either way (it helps to have completed the first two in the set), it is a great course.

It provides a very good high level picture of what is needed in ones toolbox.

Essentials: networkx, matplotlib and to a lesser extent pandas.

創建者 Kedar J

Nov 16, 2018

Great intro course to graph theory and graph analysis using applied python networkx library. The course covers a number of theoretical topics. Would recommend using a local notebook along with the lectures.

創建者 Ayon B

Nov 20, 2018

Nice course. Well presented.

創建者 To P H

Nov 11, 2018

Great course

創建者 Rahul S

Oct 08, 2018

Remarkably good explanations, and interesting selection of subtopics. Interestingly , it does not delve into Facebook or any other social media applications, and is still just as valuable as it covers Graphs in some depth. Uses Python and its NetworkX library. Knowledge of classification models and scikit-learn is needed for the 4th assignment.

創建者 David M

Oct 09, 2018

Excellent course and professor!

創建者 SagarSrinivas

Oct 02, 2018

Awesome. Worth it!