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.

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.

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!

Great overview on static and dynamic networks, the different structure of such networks and the importance of analyzing networks in society to make social impact.

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

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

I found the lecturer very good. He has a supreme didactics, with a perfect balance between intuition(psychology) and logic as regards the analysis of a claim or whichever assertion he has made throughout the course. The way in which he takes the perspective of the student and guides him/her through his lesson towards the elucidation of rather difficult concepts, shows a superior ability of teaching a skill to someone else. I am now delving into the more mathematical side of network analysis and I think this course has enabled me to construct necessary cognitive schema's with which I can develop myself even further within the field of computational social sciences. Great work guys!

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).

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!

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.

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.

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

A fantastic introduction to SNA! Even someone with little to no understanding of SNA should be able to grasp the basic of SNA with this course. I also appreciated very much the free Gephi software. I hadn't used that before, and it is very useful, especially for a free program.

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. :)

proporciona herramientas prácticas para comprender cómo se relacionan las personas y organizaciones dentro de una red, permitiendo identificar patrones de comunicación, influencia y colaboración que pueden aplicarse en distintos entornos profesionales.

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.

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.

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.

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.

Este curso para ser introductorio es muy completo, pude aprender el enfoque del Análisis de Redes Sociales, conocer sus técnicas de recopilación de información y procesamiento de datos para elaborar grafos.