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Вернуться к Социальные и экономические системы: модели и анализ

Отзывы учащихся о курсе Социальные и экономические системы: модели и анализ от партнера Стэнфордский университет

4.8
звезд
Оценки: 596
Рецензии: 130

О курсе

Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences. You can find a more detailed syllabus here: http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4...

Лучшие рецензии

MR
1 нояб. 2017 г.

Really enjoyed this course. The professor is really good and covers quite a lot of ground during the lectures. Good way to get into complex networks! Probably gonna do some studying on my own now :)

SB
10 окт. 2020 г.

Very important course. My suggestion to the Prof. if he can increase the course length and include more details that would be much better or he can come up with advance course on the same series.

Фильтр по:

101–125 из 126 отзывов о курсе Социальные и экономические системы: модели и анализ

автор: swapnil s

12 окт. 2016 г.

Great!!

автор: Andy P

18 окт. 2016 г.

great!

автор: anuj

30 мая 2017 г.

best

автор: Stylianos T

24 февр. 2017 г.

A very good introduction in social and economic networks.

I recommend this course to everyone that wants to learn how networks are formed, understand the basic concepts and get an intuition on the possible networks that he/she could form.

The professor is talking clearly so you won't have a problem in understanding him.

One thing that was missing for me was in Week 2 when he was talking about "eigenvector centrality", for me the most objective measure, the explanation was really poor and you could never understand the concept based on what the lesson offered.

автор: KM

21 авг. 2018 г.

The chemistry disciplinary knowledge cautions the utilization of the idea of diffusion because diffusion in chemistry is more of systematic random process then the idea of diffusion in this lecture. If you could enhance and clarify the Week 4 lecture of the Praeto Efficiency, Utility, and Pairwise in additional examples the brevity of the lecture could build the idea into a few slides to sharpen the idea earlier. Think about adding more examples of the Centrality examples, I thought the Centrality was interesting.

автор: Carlson O

22 апр. 2017 г.

Very comprehensive as an introductory course. The content is very actual and the lectures' flow is objective. Also, I liked the quiz inside the lectures as they helped in retain the subject. I have some hard difficulties with the mathematics as I'm very rusty with the mathematics (more than 30 years of rust). I'm from the compute science area so I would like to see more practice in algorithms. However, I would like to congratulate the Stanford University and Cousera teams for the course. Great job.

автор: Fernando I P M

3 авг. 2020 г.

Buen curso en general. Sin embargo, podría estar más actualizado en términos de aplicaciones para el año 2020. Especialmente en trabajo con datos. Además, algunas evaluaciones adolecen de elementos que no están contenidos en el material, y si bien uno puede intuir a aplicar la teoría bajo otros contextos, muchas veces los resultados no son tan intuitivos, quedando algunas dudas respecto a esos contenidos más que clarificar dicho tópico.

автор: Alejandro A R

15 июля 2018 г.

Greatly insightful and resourceful content for future research. As a recent university graduate interested in graduate school I found the course challenging meaning determination and consistency contributed to the successful completion of the course. Rewatching lectures and seeking external support helped me comprehend concepts through application.

автор: GIAN M

4 мая 2020 г.

Very interesting course, I raccomand it. It gives me a lot of notions and different view of networks, even if I'm already working with them. Very notable also the lot of references by which you can expand your knowledge and look for all the details of the field you are interested in.

Keep attention on the level, it is not for beginners :)

автор: Felipe O G C B

25 авг. 2016 г.

It's a quiet complex topic in general terms. It is well covered, but In my opinion there should be at least an exercise per video, explaining something similar to the in-video questions. It should have a demonstrative part rather than just talking about it and showing the formula.

автор: Mateus d C C

19 янв. 2021 г.

Great course, a bit complicated sometimes. The course is very structured and the classes are ordered is a natural way. The tests weren't hard and I think the course could focus more on experimental exercises.

автор: Justin K

10 дек. 2018 г.

Excellent course. The labs are the best. Pajek and Gephi will be handy for network graphing and analyzing data. Thank you Professor Matthew Jackson. Your work is very good for reference.

автор: Simon N

6 июня 2020 г.

Interesting survey of modern network theory, from Erdos-Renyi random graphs, to SIS ("flu") models, and games on networks. Rather academic at times, without the rigour.

автор: Harkeerat S

22 дек. 2016 г.

The course is vast. The Professor is to the point and doesn't lack knowledge in his field.

I'd recommend this course for anyone interested in Economics. Loved it.

автор: Michael S

24 янв. 2019 г.

I loved everything so far, the quiz questions are well selected, but, I believe there are some notions which should be explained further mathematically.

автор: Tianduo Z

1 нояб. 2016 г.

Very complex topic, very well presented. The materials are great! Would have been better to made mathematics pre-requisite clearer.

автор: Robertas K

31 мая 2020 г.

Some quizzes have wrong answers, but overall it was quite a good introduction into network analysis.

автор: ND B

17 сент. 2020 г.

I request Prof Jackson to speak more directly into the mike. At many points he is not audible.

автор: Sebastian H

15 окт. 2019 г.

Hohes Anforderungsniveau, mathematische Fähigkeiten sind zwingend erforderlich.

автор: Jose

23 янв. 2018 г.

This course is very good to introduce to the theory of networks

автор: XeRh

8 авг. 2020 г.

It's very useful if you want to learn more anout network.

автор: Dheeraj B

4 окт. 2017 г.

The discussion forums ought to be more responsive

автор: Navin N

10 дек. 2016 г.

A bit tough, but really worth the effort.

автор: Muhammad I

10 окт. 2017 г.

I'm sorry, but this course is really boring. Hopefully this lecture give more interactive approach (like animated presentation, pop up question, and so on) rather than voice of text in the slide

автор: Alexandra M

9 сент. 2020 г.

hola! me gustaría darme de baja de este curso. NO fue una buena elección.