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Avis et commentaires pour d'étudiants pour Analyse des réseaux sociaux par Université de Californie à Davis

4.7
étoiles
163 évaluations
42 avis

À propos du cours

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....

Meilleurs avis

VM
7 sept. 2020

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
29 mars 2021

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 - 25 sur 42 Avis pour Analyse des réseaux sociaux

par Everett A

27 avr. 2020

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.

par Prof. R V K

25 mai 2020

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.

par Thiago P B d M

31 mars 2020

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

par Alexis P

15 mars 2021

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).

par Miguel C

11 sept. 2020

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!

par Milena

26 juin 2020

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.

par Vidya V

10 juin 2021

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.

par Alexander P V

14 août 2020

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

par Kevin S

10 août 2021

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. :)

par Guan-Yuan W

31 mai 2020

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.

par Dilay

15 mai 2021

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.

par Igor M

24 nov. 2020

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.

par Fernando M

24 juin 2020

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.

par Gonzalo

5 juin 2020

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.

par VLADIMIR A A M

8 sept. 2020

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.

par Ruechagorn T

30 mars 2021

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!

par Garapati V

30 juin 2021

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

par Mr. M K N

16 avr. 2020

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

par Matthew P

6 juil. 2020

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

par Anran W

11 avr. 2020

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

par Mahalakshmi D

22 août 2020

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

par mohammad

2 nov. 2020

Very Usefull. Thank to Mr. Hillbert.

But it can be more technical with more exerciceses.

par Lai W W

7 avr. 2021

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

par Patricio V

7 mai 2021

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

par Domieck

28 mars 2020

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