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返回到 社交网络分析

學生對 加州大学戴维斯分校 提供的 社交网络分析 的評價和反饋

4.7
163 個評分
42 條評論

課程概述

This course is designed to quite literally ‘make a science’ out of something at the heart of society: social networks. Humans are natural network scientists, as we compute new network configurations all the time, almost unaware, when thinking about friends and family (which are particular forms of social networks), about colleagues and organizational relations (other, overlapping network structures), and about how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself). While such network structures always existed, computational social science has helped to reveal and to study them more systematically. In the first part of the course we focus on network structure. This looks as static snapshots of networks, which can be intricate and reveal important aspects of social systems. In our hands-on lab, you will also visualize and analyze a network with a software yourself, which will help to appreciate the complexity social networks can take on. During the second part of the course, we will look at how networks evolve in time. We ask how we can predict what kind of network will form and if and how we could influence network dynamics....

熱門審閱

VM
2020年9月7日

This course bringg us with many patience many perspectives and concepts in order to understan social networks. I think it was incredible for my own self-learning, and for my future researches.

RT
2021年3月29日

This is a great intro to SNA course. In just only 5 weeks, this course will walk you through key concepts, brief logic of SNA, as well as examples from the real world. Highly recommended!

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1 - 社交网络分析 的 25 個評論(共 42 個)

創建者 Everett A

2020年4月27日

Very interesting and unique concepts! The teaching is clear and at a low enough level that everyone can understand; no math or prior social science knowledge is required. However, for the in-video questions that appear, I recommend that you include a picture of what you're referencing in order to answer the question when appropriate. For example, in module 2, there were a few questions requiring us to calculate the degree, closeness degree, etc of a given network. However, the question prompt blocked the view of the network, so I had to rely on memory of the network in question to answer the question. It would've helped if there was a picture of the network in the prompt itself to serve as a reference for us to use to answer the question.

創建者 Prof. R V K

2020年5月25日

A very well explained course covering the basics of Social Network Analysis. Only thing I would like like to see more would be the use of Social Network Analysis Software and more practical analysis of the Social Networks. On the overall I thoroughly enjoyed the course and the content. Thanks for the experience. The course is definitely recommended for any beginner in Social Network Analysis.

創建者 Thiago P B d M

2020年3月31日

The course gave me a very good idea about social networks and also ideas to use in the context of social sciences

創建者 Alexis P

2021年3月15日

A great introduction to the terminology and intuition of social network analysis. Did not require too much math or computer analysis, since the focus was on understanding core concepts. What math and computer analysis there was again revolved around helping students understand the basics. Computer analysis used open-source, free software. All in all, a good course for beginners wanting a straightforward and inter-disciplinary foundation before taking more advanced classes on social networks analysis (e.g., Matt Jackson's Social and Economic Networks course).

創建者 Miguel C

2020年9月11日

My favorite course in this specialization - and one of my favorites ever! Once we've understood more theoretical concepts, we could really put it into practice and see real-life applications of this analytical tool as well as theoretical implications via computer simulations. The potential of visualizing social networks is mind-blowing!

創建者 Milena

2020年6月26日

Great course for beginners in SNA or scholars exploring new perspectives in computational social sciences. An introduction in a reach, interdisciplinary type of exploratory research that seems to be living up to its full potential in the digital age. Heartily recommending it to those looking for a first taste of SNA.

創建者 Vidya V

2021年6月10日

The course was a clear and concise overview of SNA, and as the course instructor emphasizes, it is only a crash course. Including a module on Gephi was really helpful to develop an understanding of working hands-on with data. An in-depth course could be offered to study network analysis in detail.

創建者 Alexander P V

2020年8月14日

Es un curso introductorio excelente. El profesor Martin Hilbert presenta las nociones, conceptos y técnicas de una manera sencilla, sin perder rigor y con una visión práctica de los conocimientos. Muchas gracias Coursera y Profesor Hilbert. Ha sido una excelente experiencia de aprendizaje

創建者 Kevin S

2021年8月10日

Very intense introduction into various concepts important in computational SNA (Social Network Analysis). I can highly recommend this course as well as the whole specialization to everyone interested in the field of social science in the age of digital tools. :)

創建者 Guan-Yuan W

2020年5月31日

I really enjoyed this course, I've learnt the software that specializes in SNA which was very interesting. So now I wanna take another course that relates to the social network, in order to further this part of knowledge. Keep learning.

創建者 Dilay

2021年5月15日

Education was very, very good. But I wish Turkish subtitle option had not been removed. Working this way has been challenging for me. It was very good to learn Gephi and Netlogo. But it wasn't enough for me. I worked on extra youtube.

創建者 Igor M

2020年11月24日

Very well done! Great learning tools, the teacher have good teaching skills, the little questions in the middle of the videos are a great way to process everything said, and the tests demands are accordingly the classes lessons.

創建者 Fernando M

2020年6月24日

Excelente curso, fue todo un reto tratar de entender conceptos difíciles en un idioma que no es nativo para mi, no se hizo pesado seguir el curso y es una ventaja poder retomarlo en los horarios en que uno no está trabajando.

創建者 Gonzalo

2020年6月5日

Quite interesting course to get an introduction to the analysis of social networks.

The explanations were very good, even if some times I had to review some videos because of the complexity of the subject.

創建者 VLADIMIR A A M

2020年9月8日

This course bringg us with many patience many perspectives and concepts in order to understan social networks. I think it was incredible for my own self-learning, and for my future researches.

創建者 Ruechagorn T

2021年3月30日

This is a great intro to SNA course. In just only 5 weeks, this course will walk you through key concepts, brief logic of SNA, as well as examples from the real world. Highly recommended!

創建者 Garapati V

2021年6月30日

It was really very good learning with coursera especially the mentors for social network analysis were excellent .!!!!!!!

創建者 Mr. M K N

2020年4月16日

Excellent course. Learning a lot about social network analysis. Hope to see some advance courses on this domain.

創建者 Matthew P

2020年7月6日

Loved learning the basics and getting hands on using the tools needed to analyze Social Networks. Great Course.

創建者 Anran W

2020年4月11日

A great crack course on SNA. It might be a bit difficult for newcomers, but you are making the right choice.

創建者 Mahalakshmi D

2020年8月22日

Very useful and wonderful course to enhance my knowledge. Looking forward more to learn. Thank you.

創建者 mohammad

2020年11月2日

Very Usefull. Thank to Mr. Hillbert.

But it can be more technical with more exerciceses.

創建者 Lai W W

2021年4月7日

Excellent course packed with any yet essential concepts for social network analysis.

創建者 Patricio V

2021年5月7日

This course is one of the hardest from the program, is intense but rewarding

創建者 Domieck

2020年3月28日

Learned a lot more than expected and Hilbert is a great professor