Keine Vorkenntnisse erforderlich.
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Was Sie lernen werden
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
Über dieses Spezialisierung
Praktisches Lernprojekt
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.
Keine Vorkenntnisse erforderlich.
So funktioniert die Spezialisierung
Kurse absolvieren
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Praxisprojekt
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Zertifikat erwerben
Wenn Sie alle Kurse und das Praxisprojekt abgeschlossen haben, erhalten Sie ein Zertifikat, dass Sie für potenzielle Arbeitgeber und Ihr berufliches Netzwerk freigeben können.

Es gibt 5 Kurse in dieser Spezialisierung
Computational Social Science Methods
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
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.
Analyse von sozialen Netzwerken
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
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.
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University of California, 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.
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