Computational Social Science Specialization

Start Date: 02/23/2020

Course Type: Specialization Course

Course Link:

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About Course

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.

Course Syllabus

Computational Social Science Methods
Big Data, Artificial Intelligence, and Ethics
Social Network Analysis
Computer Simulations

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Course Introduction

Computational Social Science Specialization This specialization is the last before the capstone project in which you will apply what you've learned in the previous courses to tackle a real-world social problem. You will first of all need to register for a Course Certificate ( and then complete pre-requisites ( before starting the capstone project. To gain the most out of this specialization, we highly recommend to take the following steps: 1. Read the previous courses in this specialization ( 2. Watch the short videos (in no more than 5-15 minutes) that demonstrate the key concepts and techniques of this Specialization. 3. Go through the short assignments that are provided within each module (in no more than 10-15 minutes). 4. Come back and participate in the discussions (in comments only) after each module has been completed. We look forward to seeing you in class!See you in class! The Combinational Social Science Team Course Director: Dr. Nickolay Laugel Project

Course Tag

Related Wiki Topic

Article Example
Computational social science Computational social science refers to the academic sub-disciplines concerned with computational approaches to the social sciences. This means that computers are used to model, simulate, and analyze social phenomena. Fields include computational economics, computational sociology, cliodynamics and the automated analysis of contents, in social and traditional media. It focuses on investigating social and behavioral relationships and interactions through social simulation, modeling, network analysis, and media analysis.
Computational social science Computational social science work increasingly relies on the greater availability of large databases, currently constructed and maintained by a number of interdisciplinary projects, including:
Computational social science Computational social science revolutionizes both fundamental legs of the scientific method: empirical research, especially through big data, by analyzing the digital footprint left behind through social online activities; and scientific theory, especially through computer simulation model building through social simulation. It is a multi-disciplinary and integrated approach to social survey focusing on information processing by means of advanced information technology. The computational tasks include the analysis of social networks, social geographic systems, social media content and traditional media content.
Computational science Scientific computation is most often studied through an applied mathematics or computer science program, or within a standard mathematics, sciences, or engineering program. At some institutions a specialization in scientific computation can be earned as a "minor" within another program (which may be at varying levels). However, there are increasingly many bachelor's and master's programs in computational science. Some schools also offer the Ph.D. in computational science, computational engineering, computational science and engineering, scientific computation, or all three degrees bachelor's, master's and PhD in scientific computing.
Computational science Computational science is now commonly considered a third mode of science, complementing and adding to experimentation/observation and theory. The essence of computational science is numerical algorithm
Computational science and validation of computational results. A collection of problems and solutions in computational science
Computational social choice Computational social choice is a field at the intersection of social choice theory and (theoretical) computer science (and the study of multi-agent systems). It analyzes problems arising from the aggregation of preferences of a group of agents from a computational perspective. In particular, computational social choice is concerned with the efficient computation of outcomes of voting rules, with the computational complexity of various forms of manipulation, and issues arising from the problem of representing and eliciting preferences in combinatorial settings.
Computational science Problem domains for computational science/scientific computing include:
Computational science Computational science (also scientific computing or scientific computation (SC)) is a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems. Computational science fuses three distinct elements:
Computational science Algorithms and mathematical methods used in computational science are varied. Commonly applied methods include:
Computational science Numerical analysis is an important underpinning for techniques used in computational science.
Computational Science & Discovery Computational Science & Discovery was a peer-reviewed scientific journal covering computational science in physics, chemistry, biology, and applied science. The editor-in-chief was Nathan A Baker (Pacific Northwest National Laboratory), who succeeded Anthony Mezzacappa (Oak Ridge National Laboratory) in 2011. The journal was established in 2008 and ceased publication in 2015, but all articles will remain available online.
Computational transportation science There is also an IGERT PHD program on Computational Transportation Science at the University of Illinois at Chicago.
Social science Most universities offer degrees in social science fields. The Bachelor of Social Science is a degree targeted at the social sciences in particular. It is often more flexible and in-depth than other degrees that include social science subjects.
Computational science There are also programs in areas such as computational physics, computational chemistry, etc.
Computational Science Graduate Fellowship The Computational Science Graduate Fellowship (CSGF) program is a graduate fellowship program sponsored by the United States Department of Energy and administered by the Krell Institute. Started in 1990, it awards four-year fellowships for American graduate students pursuing graduate degrees in all areas of computational science.
Computational science and/or computational mathematics. In fact, substantial effort in computational sciences
National Centre for e-Social Science The National Centre for e-Social Science (NCeSS) was a UK based centre which aimed to promote e-Social Science. It was founded circa 2003 with funding from the Economic and Social Research Council (ESRC) to investigate how innovative and powerful computer-based infrastructure and tools developed over the previous three years under the UK e-Science programme could benefit social science. The foundation of much of the work was the use of computational infrastructure that was collaboratively provisioned and worked across organisational boundaries. Collaboration was key and the computational approach was commonly known as 'Grid Computing'.
Social data revolution The analysis of large amounts of social data leads to the field of computational social science. Classic examples include the study of media content or social media content.
Computational linguistics Nowadays research within the scope of computational linguistics is done at computational linguistics departments, computational linguistics laboratories, computer science departments, and linguistics departments.