Não é necessário ter experiência prévia.Não é necessária experiência prévia.
oferecido por
Programa de cursos integrados Computational Social ScienceUniversidade da Califórnia, DavisO que você vai aprender
Discover how social networks and human dynamics create social systems and recognizable patterns
Define and discuss big data opportunities and limitations
Web scrape online data, create a social network visualization with it, and use machine learning to analyze its content
Use computer simulations to program your own artificial societies to explore business strategies and policy options
Sobre este Programa de cursos integrados
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For more information please view the Computational Social Science Trailer
Digital technology has not only revolutionized society, but also the way we can study it. Currently, this is taken advantage of by the most valuable companies in Silicon Valley, the most powerful governmental agencies, and the most influential social movements. What they have in common is that they use computational tools to understand, and ultimately influence human behavior and social dynamics.
An increasing part of human interaction leaves a massive digital footprint behind. Studying it allows us to gain unprecedented insights into what society is and how it works, including its intricate social networks that had long been obscure. Computational power allows us to detect hidden patterns through analytical tools like machine learning and to natural language processing. Finally, computer simulations enable us to explore hypothetical situations that may not even exist in reality, but that we would like to exist: a better world.
This specialization serves as a multidisciplinary, multi-perspective, and multi-method guide on how to better understand society and human behavior with modern research tools. This specialization gives you easy access to some of the exciting new possibilities of how to study society and human behavior. It is the first online specialization collectively taught by Professors from all 10 University of California campuses.
Projeto de Aprendizagem Aplicada
While no formal requisites are necessary to join this course, at the end you will web-scrape 'Big Data' from the web, execute a social network analysis ('SNA'), find hidden patterns with machine learning ('ML') and natural language processing ('NLP'), and create agent-based computer models ('ABM') to explore what might happen if we would change certain things in society.
Certificados compartilháveis
Tenha o certificado após a conclusão
Cursos 100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Cronograma flexível
Definição e manutenção de prazos flexíveis.
Nível iniciante
Aprox. 6 meses para completar
3 horas/semana sugeridas
Inglês
Legendas: Inglês
Certificados compartilháveis
Tenha o certificado após a conclusão
Cursos 100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Cronograma flexível
Definição e manutenção de prazos flexíveis.
Nível iniciante
Não é necessário ter experiência prévia.Não é necessária experiência prévia.
Aprox. 6 meses para completar
3 horas/semana sugeridas
Inglês
Legendas: Inglês
Este Programa de cursos integrados contém 5 cursos
Computational Social Science Methods
146 classificações
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.
Big Data, Artificial Intelligence, and Ethics
249 classificações
This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.
Análise de Redes Sociais
106 classificações
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.
Computer Simulations
31 classificações
Big data and artificial intelligence get most of the press about computational social science, but maybe the most complex aspect of it refers to using computational tools to explore and develop social science theory. This course shows how computer simulations are being used to explore the realm of what is theoretically possible. Computer simulations allow us to study why societies are the way they are, and to dream about the world we would like to live in. This can be as intuitive as playing a video game. Much like the well-known video game SimCity is used to build and manage an artificial city, we use agent-based models to grow and study artificial societies. Without hurting anyone in the real world, computer simulations allow us explore how to make the world a better place. We play hands-on with several practical computer simulation models and explore how we can combine hypothetical models with real world data. Finally, you will program a simple artificial society yourself, bottom-up. This will allow you to feel the complexity that arises when designing social systems, while at the same time experiencing the ease with which our new computational tools allow us to pursue such daunting endeavors.
oferecido por
Universidade da Califórnia, Davis
UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
Perguntas Frequentes – FAQ
Vou ganhar créditos universitários por concluir a Especialização?
Este Programa de cursos integrados não oferece créditos universitários, mas algumas universidades podem aceitar certificados de Programas de cursos integrados que podem ser convertidos em créditos. Entre em contato com sua instituição para saber mais. Com os cursos on-line e os certificados Mastertrack™ do Coursera, é possível ganhar créditos universitários.
Can I just enroll in a single course?
Se você se inscrever, terá 7 dias para testar sem custo e, durante este período, pode cancelar sem multa. Depois disso, não reembolsamos, mas você pode cancelar sua inscrição a qualquer momento. Veja nossa política para o reembolso total.
Posso me inscrever em um único curso?
Sim! Para começar, clique na carta de curso que lhe interessa e se inscreva. Você pode se inscrever e concluir o curso para ganhar um certificado compartilhável ou você pode auditar para ver os materiais do curso de graça. Quando você se inscrever em um curso que faz parte de uma especialização, você está automaticamente inscrito para a especialização completa. Visite o seu painel de aluno para acompanhar o seu progresso.
Can I take the course for free?
Sim, a Coursera oferece auxílio financeiro ao aluno que não possa pagar a taxa. Faça a solicitação clicando no link Auxílio Financeiro, abaixo do botão "Inscreva-se" à esquerda. Preencha uma solicitação e será notificado caso seja aprovado. Você terá que completar esta etapa para cada curso na Especialização, incluindo o Trabalho de Conclusão de Curso. Saiba mais .
Posso fazer o curso gratuitamente?
Quando se inscrever no curso, você terá acesso a todos os cursos na Especialização e ganhará um certificado quando concluir o trabalho. Se você só quiser ler e visualizar o conteúdo do curso, pode auditar o curso gratuitamente. Se não puder pagar a taxa, você pode solicitar ajuda financeira.
Este curso é realmente 100% on-line? Eu preciso assistir alguma aula pessoalmente?
Este curso é totalmente on-line, então não existe necessidade de aparecer em uma sala de aula pessoalmente. Você pode acessar suas palestras, leituras e atribuições a qualquer hora e qualquer lugar, via web ou dispositivo móvel.
Quanto tempo é necessário para concluir a Especialização?
No background knowledge is necessary to work through this Specialization.
Do I need to take the courses in a specific order?
We highly encourage you complete the courses in the order specified as they do build on one another.
Will I earn university credit for completing the Specialization?
No, this specialization does not offer University credit.
Vou ganhar créditos universitários por concluir a Especialização?
This Specialization is the result of a collective effort with contributions from Professors from all 10 campuses of the University of California. It is coordinated by Martin Hilbert, from UC Davis, and counts with lectures from:
1) UC Berkeley: Joshua Blumenstock, Prof. iSchool; Stuart Russell, Professor of Computer Science and Engineering.
2) UC Davis: Martin Hilbert, Prof., Dpt. of Communication & Seth Frey, Prof., Dpt. of Communication & Cynthia Gates, Director of the IRB.
3) UC Irvine: Lisa Pearl, Prof. Cognitive Sciences.
4) UC Los Angeles: PJ Lamberson, Assistant Prof. Communication Studies.
5) UC Merced: Paul Smaldino, Prof. Cognitive and Information Sciences.
6) UC Riverside: Christian Shelton, Prof. Computer Science.
7) UC San Diego: James Fowler, Prof. Global Public Health and Political Science.
8) UC San Francisco: Maria Glymour, Associate Prof. School of Medicine, Social Epidemiology & Biostatistics.
9) UC Santa Barbara: René Weber, Prof. Dpt. of Communication & Media Neuroscience Lab (with Frederic Hopp).
10) UC Santa Cruz: Marilyn Walker, Prof. Computer Science, Director, Computational Media.
What do students say AFTER COMPLETION?
"Highly enjoyable and most importantly, giving me exceptionally important skills to fulfill my job requirements at a new position in Munich. You may be interested to know the impact of your course on salary and in my case, the knowledge and certification gained adds about another Euro 20.000 on the annual salary (taking it to about Euro 120.000 p.a.)."
· "My overall impression of this was: I can't wait to use this for other stuff!!"
· "Best course I have taken. I wish more online courses structured like this would be offered."
· "The fact that these tools are so easily usable and attainable is incredible in my mind. Not only do we have access to them like we have access to things like Facebook and Twitter, but they're FREE."
· "I had previously attempted to learn Python but gave up because I thought that I wasn't capable of learning it, as I was so intimidated. Taking your course opened my eyes, not only regarding how useful it would be in my fields of study, but also by showing me that it's not so scary after all. I am now planning on spending my summer working on a Python project, and I am confident that I can do so. I also plan on taking more CS-centric courses in the future."
· "I absolutely think that these tools could be used in my future jobs, or even as a personal reflection. If you scrape and analyze the comments/reactions that your business gets on Youtube, Twitter, Instagram, etc., what does their language use say about how they interact with your brand — or what your brand brings out in them?"
· "Wow, this is cool and fun stuff. Even though I may not pursue anything social-science related in the near future, it is still nice to learn and get to experience all of these tools that computational social science offers and benefits in all kinds of careers and fields of study."
· "I did my MA in Social Work in India. I am trying to make a come-back in my field after a long career break. I had been hearing Big Data and Data Science everywhere and wondered if there is a link between these and Social Sciences. This specialization gave me needed answers and is helping me to gain very useful skills... Thank you so much for bringing this specialization. You are a very good instructor and made these courses are a smooth sail."
· "My career aspiration is to be a digital marketing expert. These computational tools have enormous implications for the field."
· "I particularly enjoyed the web-scraping for some reason. It feels very advanced although its very easy. ...It seems to be a very fast and efficient way of grabbing data."
· "I enjoyed playing around with machine learning! ...It was also amazing to me how quickly it was able to grasp and learn our input in seconds. It makes me wonder how much more technology will advance in these next few years... It's scary but fascinating."
· "The most interesting aspect was the fact that these tools are all free and online. In the past, only researchers at well-funded universities had access to programs like the ones we used in all of our labs. But now, even someone without much technical knowledge on complex software can use these tools."
· "I am so surprised that these tools are available to anyone through a simple download, and even more so that they are very user friendly and easy to learn how to navigate. I plan on starting a clothing line company in the future and I think it will be really helpful for me to be able to analyze so much online data."
· "As an Environmental Policy Analysis and Planning major, I was fascinated to learn that there is a feasible way to simulate policy implementation and impact multiple times within a short span of time."
· "UCCSS has allowed me to feel more confident in my abilities with a computer and to better understand companies like Facebook or Twitter. ...these tools really are powerful but also dangerous. ...It allows powerful individuals to manipulate ideas."
· "Throughout the course, the content was challenging, but when it was finally applied to the labs at the end of each module, it was really rewarding to see everything play out. It was even more rewarding when it made sense too! ... I'm really glad I took this course! It was definitely a challenge, but I'm glad I got to experience and learn about so many topics I never knew even existed."
· "It was fun seeing the results of the code that I made, and I never thought that I would be doing something like this in my life. The results also showed me what the society would look like.... Social network analysis and web scraping could be the tools that I use in my future job as all the internship that I'm looking now all related to social media or digital media."
· "I really really loved that this class let me learn hands-on and gave me experience with tools that have real world application and combine STEM & social science. I think that a lot of these tools are useful far beyond homework activities."
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