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.
Overview
Syllabus
Course 1: Computational Social Science Methods
- Offered by University of California, Davis. This course gives you an overview of the current opportunities and the omnipresent reach of ... Enroll for free.
Course 2: Big Data, Artificial Intelligence, and Ethics
- Offered by University of California, Davis. This course gives you context and first-hand experience with the two major catalyzers of the ... Enroll for free.
Course 3: Social Network Analysis
- Offered by University of California, Davis. This course is designed to quite literally ‘make a science’ out of something at the heart of ... Enroll for free.
Course 4: Computer Simulations
- Offered by University of California, Davis. Big data and artificial intelligence get most of the press about computational social science, ... Enroll for free.
Course 5: Computational Social Science Capstone Project
- Offered by University of California, Davis. CONGRATULATIONS! Not only did you accomplish to finish our intellectual tour de force, but, by ... Enroll for free.
- Offered by University of California, Davis. This course gives you an overview of the current opportunities and the omnipresent reach of ... Enroll for free.
Course 2: Big Data, Artificial Intelligence, and Ethics
- Offered by University of California, Davis. This course gives you context and first-hand experience with the two major catalyzers of the ... Enroll for free.
Course 3: Social Network Analysis
- Offered by University of California, Davis. This course is designed to quite literally ‘make a science’ out of something at the heart of ... Enroll for free.
Course 4: Computer Simulations
- Offered by University of California, Davis. Big data and artificial intelligence get most of the press about computational social science, ... Enroll for free.
Course 5: Computational Social Science Capstone Project
- Offered by University of California, Davis. CONGRATULATIONS! Not only did you accomplish to finish our intellectual tour de force, but, by ... Enroll for free.
Courses
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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.
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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.
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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!
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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.
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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
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Reviews
4.8 rating, based on 133 Class Central reviews
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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. -
It gave bird eye view on several of the topics lab work was little improved than previous courses in the specialization
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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.!!!
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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 -
I took the SNA module, and it was a great introduction to the general theory and software used in this field. This is a great way to familiarize with such a method. It combines video lectures and hands-on workshops.
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great specialization content and useful tools to apply to analyze, modelling and simulling behaviours according with different environment conditions
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A well structured course, with lectures that are clear and relevant. There is a lot of theory easily explained by providing examples that allow students with different backgrounds to follow without problems the curricula. As a compliance & ethics professional, I highly recommend it as opened my eyes to methodologies and tools I could use for my work.
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Minjoo LeeThis 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… -
I'm excited to go through the course! It was an amazing adventure to the world of CSS, machine learning, scientific approaches - induction and deduction, agent based modelling and web scraping. I was able to apply the knowledge I got in this course…
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Awesome class! I took it as a UCD student to fill my units in my senior year, turned out I believe this may be one of the best classes to take as a freshman/transfer just yet! Very good intro to different topics in CSS and introduce different possible applications to AI, no coding/math background required for this course, but of course it can lead to very coding/math-intense course if you find it interesting enough to do it for a living. A bit time-consuming for a filler class, but strongly recommended who wants to take a sneak peak in the AI world.
Bonus: the professor is German, so his German humor is a bit of a highlight. It's the kind of joke that you want to laugh but it's hard to laugh, I loved it. -
Great course, I´m studying my Phd, and this course has help me to understand the complex network. I hope to be able to advance in the following modules and courses that you have
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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.
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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.
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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.
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Very good overview of social network analysis. I learned a lot, and look forward to taking more classes on this topic.
A word of warning though: The hands-on project for this course requires a computer that will run Gephi. It will not work on a phone or tablet. This should be listed in the course requirements. -
Hi
It was a very good course for getting to know the topic of social network analysis and I recommend it. This course does not go deep in any way and very quickly and superficially reviews all the topics in this field, and while it does not take much of your time, it gives you an almost complete view of this field. -
This was a very helpful lab and course I think i will be using tools like Gephi to my work as it can help with the networking analysis of how our business operates. I did fine all the practical labs more useful and easy to understand than the methodologies and theory aspect but that's just me.
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For me as a social science researcher this specialization was an interesting window to the new achievements of computer scientists in social research. I think in making a new intersection between sociology tradition and these new approaches we are just at the beginning.
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I feel there should be a mind map towards the start of every module and towards the end, and a mind map for the whole course placing the concepts and tools discussed, so that it is easy to recall and place concepts in their right place in the conceptual hierarchy
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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.
