Computational Social Science

University of California, Davis via Coursera Specialization

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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.

Syllabus

Course 1: Computational Social Science Methods
Course 2: Big Data, Artificial Intelligence, and Ethics
Course 3: Social Network Analysis
Course 4: Computer Simulations
Course 5: Computational Social Science Capstone Project

Courses

144 reviews
11 hours 20 minutes
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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.
6 reviews
8 hours 31 minutes
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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.
34 reviews
13 hours 38 minutes
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CONGRATULATIONS! Not only did you accomplish to finish our intellectual tour de force, but, by now, you also already have all required skills to execute a comprehensive multi-method workflow of computational social science. We will put these skills to work in this final integrative lab, where we are bringing it all together. We scrape data from a social media site (drawing on the skills obtained in the 1st course of this specialization). We then analyze the collected data by visualizing the resulting networks (building on the skills obtained in the 3rd course). We analyze some key aspects of it in depth, using machine learning powered natural language processing (putting to work the insights obtained during the 2nd course). Finally, we use a computer simulation model to explore possible generative mechanism and scrutinize aspects that we did not find in our empirical reality, but that help us to improve this aspect of society (drawing on the skills obtained during the 4th course of this specialization). The result is the first glimpse at a new way of doing social science in a digital age: computational social science. Congratulations! Having done all of this yourself, you can consider yourself a fledgling computational social scientist!
53 reviews
12 hours 49 minutes
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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.
5 reviews
10 hours 10 minutes
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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.

Taught by

Martin Hilbert

Reviews

4.8 rating, based on 144 Class Central reviews

4.6 rating at Coursera based on 1230 ratings

Start your review of Computational Social Science

Elham Nazif @Elham
5

Great introductory course on Computational Social Science, a fairly new and exciting feel of Social Science.

Martin Hilbert gives a great overview of the computational techniques and social theories that Computational Social Scientists use in their work, from empirical to the analytical, and from theories to simulation.

The course length is sweet, and the material given isn't difficult to understand, although it requires some basic familiarity with maths and using computers.

This course should be taken by anyone who is interested in the field of Computational Social Science.
Anonymous

It could have been explained in a more simple and easy language with more practical tutorial labs to understand and learn how the data is studied and analysed.
Anonymous

Liked the course. Acquired a few novel skills. Looking forward to applying the skills in daily life social science cases.
Anonymous

This is an excellent course for beginners just learning about computational social sciences, or for those looking to brush up on their knowledge. The course is nicely laid out, with each section featuring an interactive lab relating to social network analysis, that helps you apply what you have learned in a fun, effective way. I highly recommend this course to anyone interested in careers involving social network analysis or human behavior.
Anonymous

I liked the course content, and the sessions were engaging an easy to follow. I wish there were more opportunities to do more hands-on labs and to practice the content, but then as stated by the facilitator, this course is meant to provide you with a "10,000 ft. bird's eye view" of social network analysis, meaning that content had to be concise.
The course definitely triggered my interest in learning a lot more about SNA and to become more proficient in the practice.
Anonymous

The course was very well structured, covering basics of Social Network Analysis. The lectures were very clear and the concepts were well-explained. One thing I really liked was that the lectures revisited (and asked small quizzes) on core concepts covered in previous lectures. Will look forward to more advanced courses on this topic. Thanks!
Anonymous

I really appreciated the course, especially because we immersed ourselves in some interesting practical exercises. You can really get a grasp of the subject matter and be inspired by the content. I also think it's well-structured didactically and is so understandable, even for people who graduated a while ago and want to get up to speed.
Minjoo Lee
2

This course exceeded my expectations tremendously! I was able to learn from professors from every UC campus which gave me expertise knowledge in every area. When I signed up for the course I immediately understood that this course is perfect for peo…

This course exceeded my expectations tremendously! I was able to learn from professors from every UC campus which gave me expertise knowledge in every area. When I signed up for the course I immediately understood that this course is perfect for people like me who wanted to see what computational social science is about. It very interesting as the interactive labs allow you to actually use what you have learned and what it means. Being a visual learner it was great to have a course like this where it is not just lectures, but rather interactive assignments that you are able to do while given a step by step process. All the things I have learned throughout the course I will be able to use when I want later in life. I definitely recommend taking this course as you will be able to learn the beginnings aspects of SNA, ethics, and computer simulations.
Anonymous

It was a very helpful course for me. Helped me translate some of my intuitions into science. Empowered me with some cool tools to use to visualise graphs, static and dynamic. Now that I have enough food for thought, I'll take some more advanced courses in the subject.
Anonymous

I took this course through UC cross enrollment 2 years ago . The course content was very up-to-date, and included guest lectures from professors from a very wide range of disciplines, so it would be interesting to students regardless of their majors. Despite not being a student in the social sciences (my majors are in humanities and arts,) this class has equipped me with a lot of basic digital literacy (e.g, present issues of privacy with photo filter apps, epidemic modeling) that has been helpful in everyday life. I hope Prof. Hilbert can make more of his classes available.
Anonymous

This was a well organized, well presented crash course in Social Network Analysis. It covers a lot of material in a short period of time.
Anonymous

Having taken this course as an undergraduate, I can honestly say that this course effectively simplified complex concepts through practical projects. Computational Social Science might seem 'scary' at first, but Dr. Hilbert managed to provide diverse content, superb guidance, and enough flexibility to be creative to make the learning experience sufficiently challenging and extremely rewarding. Whether you're taking this course as first-timer or seasoned researcher, one can narrow or expand their interests while maintaining a comprehensive learning experience.
Atul Pandey @atulpandey82l

It gave bird eye view on several of the topics lab work was little improved than previous courses in the specialization
Anonymous

This class taught me so much. The most valuable part I found, unfortunately is during this recent pandemic. I found that after taking this class my understanding of the developing social transmission was increased. The assignments helped me understand the graphics being used, because we created similar basic ones in this class.
Anonymous

The course was an insightful and engaging experience. It provided a strong foundation in using computational methods to analyze social phenomena; covering key concepts such as data analysis, machine learning, social network analysis, etc. Overall, it was a valuable introduction to the field and sparked my interest in further exploring computational approaches to social research.
Anonymous

I learned a lot from this course. It was well structured, the presenters were easy to understand and the prompts and assessment tasks were good. Highly recommend as an introduction to social network analysis.
Anonymous

This course was thorough and challenging without being too overwhelming. I really appreciated up-beat attitude of the instructor. I really enjoyed the incorporation of integrative labs for hands-on experience in this remote setting. I definitely feel like I learned a great deal in this course.
Anonymous

I was looking for more hands on experiences. the course is highly theoritical and introduced many new concepts but without depth
Anonymous

It was really a good learning experience. I think the didactic approach was pertinent for the begginer level, form the simple to the complex. Other strenght, is the accuracy of the presentations and especially of the tests, It was for me a satisfying challenge, It wasn´t easy but it was motivator for me. I want to congratulate to Martin Hilbert he is an expert and good teacher.
Thank you Coursera and UCDavis for this course
Gustavo Andrade
Anonymous

I think this was a very good course. The only reason I gave it 4/5 was because with the emergence of AI with "All Data", this course also needs to address data privacy and problem of control , in other words ethics plays a major role . I strongly advise to revise this course to add materials on ethics and limitations of the solutions It covered in this course. I hope my feedback would be taken positively. Overall I really like this course and struggle then learned.!!!