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Opiniones y comentarios de aprendices correspondientes a Computational Social Science Methods por parte de Universidad de California, Davis

4.7
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238 calificaciones
63 reseña

Acerca del Curso

This course gives you an overview of the current opportunities and the omnipresent reach of computational social science. The results are all around us, every day, reaching from the services provided by the world’s most valuable companies, over the hidden influence of governmental agencies, to the power of social and political movements. All of them study human behavior in order to shape it. In short, all of them do social science by computational means. In this course we answer three questions: I. Why Computational Social Science (CSS) now? II. What does CSS cover? III. What are examples of CSS? In this last part, we take a bird’s-eye view on four main applications of CSS. First, Prof. Blumenstock from UC Berkeley discusses how we can gain insights by studying the massive digital footprint left behind today’s social interactions, especially to foster international development. Second, Prof. Shelton from UC Riverside introduces us to the world of machine learning, including the basic concepts behind this current driver of much of today's computational landscape. Prof. Fowler, from UC San Diego introduces us to the power of social networks, and finally, Prof. Smaldino, from UC Merced, explains how computer simulation help us to untangle some of the mysteries of social emergence....

Principales reseñas

AA
22 de mar. de 2021

This introduction course of CSS Methods is nicely structured. Prof. Hilbert and the others are great presentations. I enjoyed the course and will definitely continue to the next course.

DA
6 de jul. de 2020

Excellent course and the instructor(s) make it even more engaging. I love Martin Hilbert and his explanations, examples. I enjoyed course 1 alot and continuing course 2 at the moment.

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1 - 25 de 65 revisiones para Computational Social Science Methods

por Amalia A

23 de mar. de 2021

This introduction course of CSS Methods is nicely structured. Prof. Hilbert and the others are great presentations. I enjoyed the course and will definitely continue to the next course.

por Deleted A

7 de jul. de 2020

Excellent course and the instructor(s) make it even more engaging. I love Martin Hilbert and his explanations, examples. I enjoyed course 1 alot and continuing course 2 at the moment.

por Diego A P P

17 de jul. de 2020

Excellent course. I learned practical skills.

por Gil D

9 de jun. de 2020

Very good course ; 5 stars

por Rangdan C

30 de jun. de 2021

love it!

por Noor Q

7 de mar. de 2020

This is a really fun course, even though I had some knowledge in the topic it is presented in a fun creative way that I learned a lot and never felt bored.

There were few issues though, the peer graded assessment asks for 5 pictures and then awards people who post more than 5 without saying that is the case in the instructions, as a teacher myself I would never expect students to provide more than they are asked for and penalize them if they don't.

I also found some of the machine learning lecture in week 3 confusing, especially considering I had some background which made me able to keep up, others might struggle.

por Anirban P

17 de ago. de 2020

The first hands-on assignment using web-scraper was really interesting. I would have liked to see a few more similar assignments n other Modules as well. Overall the course was good at providing basic information related to different computational methods that can be used in Social sciences, in a fun and interactive way.

por Alexander P V

29 de may. de 2020

I thank Coursera and the community of teachers, especially Professor Hilbert for the knowledge and training provided. It is an excellent introductory course in Computational Social Sciences. It illustrates very well the new possibilities that we have to investigate in Social Sciences taking full advantage of new tools and methods, in addition to a new theoretical field that is recast with the knowledge obtained by researchers before the digital revolution. The course has practical exercises with which you begin to develop skills to identify sources of information, extract, and process the data available on the internet. The concepts are approached from different perspectives facilitating their learning. Well, you can watch videos, read and observe mind maps.

por Guan-Yuan W

31 de may. de 2020

This course gave me an overview of the core concepts of computational social science, including big data, machine learning, social network and computer simulation and so forth. Nowadays, owing to massive digital footprints left behind today’s social interactions, there are more and more researches done effectively and accurately by computational means, for example, helping governmental policymaker to make better decisions via data science, studying human behaviour to help our lives better via social network analysis, etc. Thus, as an economics master student, I'd like to apply this sort of tools to develop my personal research.

por OLAYEMI H R

3 de may. de 2020

I have benefited a lot in this course so far. A lot of concepts i have not heard before and the ones i didn't have clear pictures on before are now clearer. The web scraping was so interesting such that I started developing interest in learning how to web scrape using python. The machine learning tool also, though not really deep, but I enjoyed it. Learning how to use digital footprint for detecting poverty level was really amazing, it boost my moral in understanding how big data is so important in this 21st century. The knowledge garnered in social networking analysis was quite massive. I am happy I took this course.

por Amelia S

19 de may. de 2020

The lectures are very well structured and the flow of each lectures flows great; reference support and reading material is very satisfying; Presentation support such as drawings, slides, cartoons is very interesting; the way the lecturer teaches is very passionate and very interesting; the speed of the lecturer is suitable to my ability to listen and comprehend; the voice and pronunciation are clear and easy to understand; the material taught is very useful, interesting and up to date; this lecture will be very useful for my career in the future; this lecture will be very useful for real life in the future

por Ernesto E

25 de sep. de 2020

The course gives me a deeper understanding of social computing. It also re-enforced my assumption that it is possible to offer courses related to ICT for Sustainable Development wherein the 3 pillars of SD - social, economic, and environment can be offered as 1 specialization course in Master and Doctorate degree courses or a Post-Doctorate course. It can be a Doctor in ICT for Sustainability Science.

por Fausto B D S T

6 de jul. de 2021

The course met my expectations, providing a good overview of the several tools and applications of computational social science. I particularly enjoyed the discussion about philosophy of science (empirical, analytical, theoretical etc.) and the hands-on lab (webscrapping).

Thanks for the learning experience and congratulations for the good work developing this course!

Best regards. Fausto, from Brazil.

por Quji B

12 de oct. de 2020

Thank you for this wonderful course. I learned a lot, including what CSS is all about and what are the parts that fill different parts of it. I understood what is Big Data and ML and these terms to me seem not scary but actually approachable now. Would have been great to have more exercises and reading material (yes, the instructors got me hooked on the topic).

por Maria X R

27 de may. de 2020

I was really impressed by how relevant the topics presented were, and especially by the variety of fields that are shown. This would not have been possible if professors from different backgrounds had not collaborated to create such a rich course like this one. After this course, I am really looking forward to the upcoming courses in the specialization.

por Daniel V

10 de nov. de 2020

This is a really good and engaging course! The instructors are great, the examples are relevant, and the interactive questions are funny, which helps in the learning process. I recommend the course for anyone who is interested in learning the basics of computational social science and start to understand the techniques used for that!

por Satyaveer P

16 de sep. de 2020

An excellent course to enter the field of Computational Social Science. Even if one doesn't end up in this field, it gives a fascinating insight into how society works in general. I really liked the hands-on approach. The examples used in the lectures were perfect!

por Muxin L

23 de mar. de 2020

Great introduction to the specialization series on computational social science. Some technical tools are introduced but the course is largely a primer to what's currently possible in the digital age. Recommend for anyone curious about what this discipline is.

por Tuhina R

5 de feb. de 2021

The course was very well planned. The course does not need any prior specialization and anybody with diligence and intent can acquire the skill taught through this course with ease

por Patrick A T

11 de jul. de 2020

This is a great course to take as an introduction to Computational Social Science. I hope the rest of the Specialization is just as engaging, relevant, and informative.

por Ilgi T

9 de ago. de 2021

V​ery useful and clear introductory course! Thanks Prof. Martin Hilbert for the amazing content. I cannot wait to take all the other courses in this specialization.

por Miguel C

19 de ago. de 2020

Don't expect to dive deep into computational tools, but it's a great introductory course into Computational Social Science and the professors are great.

por mohammad

24 de ago. de 2020

Very good and above my expextation. Handy package of theoritical and practical approach. I think you, as a social reasercher should take this too.

por Dwayne R

21 de nov. de 2021

F​antastic introduction to social network analysis and how modeling and machine learning are used to plan or predict future actions from past data.