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Martin Hilbert
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?
Read more
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?
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/j8w4c8/computational
What's inside
Syllabus
Computational Social Science (CSS)
In this module, you will be able to examine the history and current challenges faced by social science through the digital revolution. You will be able to discuss the mystery at the core of society: social emergence. You will be able to recall the fundamental building blocks of the scientific method and how they apply to the new computational tools we now have available. You will be able to defend what people mean when they say that ‘social studies’ are currently maturing to become a ‘real science’.
Read more
Syllabus
Computational Social Science (CSS)
In this module, you will be able to examine the history and current challenges faced by social science through the digital revolution. You will be able to discuss the mystery at the core of society: social emergence. You will be able to recall the fundamental building blocks of the scientific method and how they apply to the new computational tools we now have available. You will be able to defend what people mean when they say that ‘social studies’ are currently maturing to become a ‘real science’.
Read more
Example of Computational Social Science: Data Science
In this module, you will be presented with an example of how computational social science is applied in the real world through a case study. You will be able to discuss examples of digital footprint and describe how computational social science is applied. You will practice an activity and be able to configure a machine to create a database that can later be used for analysis.
Examples of CSS: Machine Learning & AI
In this module, you will be able to discover how artificial intelligence can convert news stories into a real-time observatory of global unrest and potential terror attacks, and how brain scans can be used to reveal aspects of your moral values. You will be able to practice interacting with artificial intelligence that can interpret your art skills.
Examples of CSS: Social Networks and Computer Simulations
In this module, you will be able to discover how social networks and human dynamics create systems that are larger than you and me: social systems. You will be able to discuss how social networks and human dynamics follow recognizable patterns. You will be able to identify how social network analysis and computer simulations are currently quite successful in untangling some of the mysteries of social emergence.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores the emerging field of computational social science, which combines social science with computational techniques to gain insights into human behavior
Taught by a team of experienced professors from UC Berkeley, UC Riverside, UC San Diego, and UC Merced, who are recognized for their research in computational social science
Presents real-world examples of how computational social science is applied in fields such as data science, machine learning, and social network analysis
Suitable for students with a background in social science, computer science, or a related field who are interested in learning about the latest advances in computational social science
Requires students to engage with interactive materials and participate in online discussions
Assumes that students have a basic understanding of statistics and research methods
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Reviews summary
Positively reviewed social science methods course
Learners say this well received course offers a broad overview of computational social science methods. Instructors are engaging and present relevant topics with real-world examples and hands-on activities. The course introduces big data, machine learning, social network, and computer simulation concepts. Note that this course does not dive deeply into technical tools for implementing these methods.
The course includes interactive exercises and assignments that make the learning process more engaging.
"Really interesting and helpful info. Lots of real world examples makes the info easier to digest."
"The course was very well planned."
"This is a really good and engaging course! The instructors are great, the examples are relevant, and the interactive questions are funny, which helps in the learning process."
The course covers a wide range of computational social science concepts and serves as a helpful introduction to this exciting field.
"Got a rudimentary level understanding of the power of computational tools like ML, SNA and ABM can help in analysis of human dynamics"
"It really helped me in finding that how many things can I do with social data and what new problems can be solved with social networks"
"This course gave me an overview of the core concepts of computational social science, including big data, machine learning, social network and computer simulation and so forth."
Instructors are passionate, engaging, and use clear explanations.
"Excellent course. The instructor is very entertaining made the course enjoyable and the assignments were interesting."
"This is a really fun course, even though I had some knowledge in the topic it is presented in a fun creative way that I learned a lot and never felt bored."
"The course met my expectations, providing a good overview of the several tools and applications of computational social science."
This course does not deeply explore computational tools or technical skills.
"Don't expect to dive deep into computational tools, but it's a great introductory course into Computational Social Science and the professors are great."
"Very good, but I would have liked to know more about technical skills than theoretical anedotes. Thank you!"
"Provides some information, but too scattered to make me feel like I have a comprehensive understanding of what's going on on CSS."
"I’m so confused by all the glowing reviews of this course. Am I taking a different course than everyone else?"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Computational Social Science Methods with these
activities:
Review Calculus Concepts
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Strengthens students' foundational understanding of calculus, which is essential for understanding data analysis techniques.
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Calculus
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-
Review basic calculus concepts such as derivatives and integrals.
-
Practice solving calculus problems.
Participate in Peer Study Groups
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Enables students to collaborate with peers, share insights, and enhance their understanding of course material.
Browse courses on
Computational Social Science
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-
Form study groups with fellow students.
-
Meet regularly to discuss course topics, assignments, and projects.
Read 'Data Science for Business'
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Review the fundamentals of data science and its applications in business.
View
Data Science for Business: What You Need to...
on Amazon
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-
Read chapters 1-3 to understand the basics of data science.
-
Read chapters 4-6 to learn about data mining and machine learning.
-
Read chapters 7-9 to explore the applications of data science in business.
-
Complete the exercises and quizzes at the end of each chapter.
12 other activities
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Solve LeetCode Problems
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Practice solving coding problems to improve your problem-solving skills and coding proficiency.
Browse courses on
Coding
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Sign up for a LeetCode account.
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Choose a problem to solve.
-
Read the problem statement and understand the requirements.
-
Come up with a solution to the problem.
-
Implement the solution in the coding language of your choice.
Read 'The Data Revolution'
Show steps
Examines the impact of data on society and provides insights into the opportunities and challenges of computational social science.
View
Reinventing Capitalism in the Age of Big Data
on Amazon
Show steps
-
Read chapters 1-3 to understand the history and impact of data on social science.
-
Read chapters 4-6 to explore the opportunities and challenges of computational social science.
-
Read chapters 7-9 to gain insights into using data to address societal issues.
Complete Data Science Tutorial Series
Show steps
Provides step-by-step guidance on using data science tools and techniques, reinforcing course concepts.
Browse courses on
Data Science
Show steps
-
Follow along with the tutorial series to learn data preprocessing and analysis techniques.
-
Practice working with real-world datasets.
-
Complete the exercises and quizzes to test your understanding.
Join a Study Group
Show steps
Engage in discussions and collaborate with other students to reinforce your understanding of the course material.
Show steps
-
Find a study group to join.
-
Attend study group meetings regularly.
-
Participate in discussions and ask questions.
-
Help other students with their understanding.
Volunteer at a Non-Profit Organization
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Gain practical experience in the field of social science by volunteering at a non-profit organization.
Show steps
-
Research different non-profit organizations that you are interested in.
-
Contact the organization and inquire about volunteer opportunities.
-
attend a volunteer training session.
-
Volunteer at the organization on a regular basis.
Solve Practice Problems
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Strengthen your understanding of statistical concepts and research methods by solving practice problems.
Browse courses on
Statistics
Show steps
-
Find practice problems online or in textbooks.
-
Read the problem statement carefully.
-
Identify the relevant statistical concepts and methods.
-
Solve the problem using the appropriate techniques.
Build a Machine Learning Model
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Apply the concepts of machine learning to build a model that solves a real-world problem.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset that you want to work with.
-
Clean and prepare the data.
-
Select a machine learning algorithm that is appropriate for the data and the problem you are trying to solve.
-
Train the model and evaluate its performance.
-
Deploy the model to a production environment.
Contribute to an Open-Source Project
Show steps
Contribute to an open-source project related to data science to enhance your practical skills and knowledge.
Browse courses on
Python
Show steps
-
Find an open-source project that you are interested in.
-
Create a GitHub account and fork the project.
-
Make changes to the code and submit a pull request.
-
Review code from other contributors and provide feedback.
Solve Data Analysis Problems
Show steps
Challenges students to apply data analysis techniques to solve real-world problems, strengthening their skills.
Browse courses on
Data Analysis
Show steps
-
Collect data from a variety of sources.
-
Clean and prepare the data for analysis.
-
Apply appropriate data analysis techniques to extract insights.
-
Interpret the results and draw conclusions.
Create a Comprehensive Course Summary
Show steps
Helps students organize and reinforce their learning by creating a centralized repository of course materials.
Browse courses on
Computational Social Science
Show steps
-
Review lecture notes, textbooks, and other course materials.
-
Summarize key concepts and insights in a structured format.
-
Include relevant examples, diagrams, and elaborations.
Create a Course Summary
Show steps
Consolidate and organize your course materials for effective review and retention.
Show steps
-
Gather all of your course materials, including notes, assignments, and quizzes.
-
Organize the materials into a logical order.
-
Create a summary of each lecture or module.
-
Highlight the key concepts and ideas.
Develop a Computational Social Science Research Proposal
Show steps
Encourages students to apply course concepts to develop a proposal for their own computational social science research project.
Browse courses on
Computational Social Science
Show steps
-
Review relevant literature and gather data.
-
Identify a research topic and formulate a hypothesis.
-
Design a data analysis plan.
-
Write a research proposal outlining your research plan.
Review Calculus Concepts
Show steps
Strengthens students' foundational understanding of calculus, which is essential for understanding data analysis techniques.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Review basic calculus concepts such as derivatives and integrals.
- Practice solving calculus problems.
Participate in Peer Study Groups
Show steps
Enables students to collaborate with peers, share insights, and enhance their understanding of course material.
Browse courses on
Computational Social Science
Show steps
Show steps
Show steps
- Form study groups with fellow students.
- Meet regularly to discuss course topics, assignments, and projects.
Read 'Data Science for Business'
Show steps
Review the fundamentals of data science and its applications in business.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the basics of data science.
- Read chapters 4-6 to learn about data mining and machine learning.
- Read chapters 7-9 to explore the applications of data science in business.
- Complete the exercises and quizzes at the end of each chapter.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Solve LeetCode Problems
Show steps
Practice solving coding problems to improve your problem-solving skills and coding proficiency.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Sign up for a LeetCode account.
- Choose a problem to solve.
- Read the problem statement and understand the requirements.
- Come up with a solution to the problem.
- Implement the solution in the coding language of your choice.
Read 'The Data Revolution'
Show steps
Examines the impact of data on society and provides insights into the opportunities and challenges of computational social science.
View
Reinventing Capitalism in the Age of Big Data
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to understand the history and impact of data on social science.
- Read chapters 4-6 to explore the opportunities and challenges of computational social science.
- Read chapters 7-9 to gain insights into using data to address societal issues.
Complete Data Science Tutorial Series
Show steps
Provides step-by-step guidance on using data science tools and techniques, reinforcing course concepts.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Follow along with the tutorial series to learn data preprocessing and analysis techniques.
- Practice working with real-world datasets.
- Complete the exercises and quizzes to test your understanding.
Join a Study Group
Show steps
Engage in discussions and collaborate with other students to reinforce your understanding of the course material.
Show steps
Show steps
Show steps
- Find a study group to join.
- Attend study group meetings regularly.
- Participate in discussions and ask questions.
- Help other students with their understanding.
Volunteer at a Non-Profit Organization
Show steps
Gain practical experience in the field of social science by volunteering at a non-profit organization.
Show steps
Show steps
Show steps
- Research different non-profit organizations that you are interested in.
- Contact the organization and inquire about volunteer opportunities.
- attend a volunteer training session.
- Volunteer at the organization on a regular basis.
Solve Practice Problems
Show steps
Strengthen your understanding of statistical concepts and research methods by solving practice problems.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks.
- Read the problem statement carefully.
- Identify the relevant statistical concepts and methods.
- Solve the problem using the appropriate techniques.
Build a Machine Learning Model
Show steps
Apply the concepts of machine learning to build a model that solves a real-world problem.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset that you want to work with.
- Clean and prepare the data.
- Select a machine learning algorithm that is appropriate for the data and the problem you are trying to solve.
- Train the model and evaluate its performance.
- Deploy the model to a production environment.
Contribute to an Open-Source Project
Show steps
Contribute to an open-source project related to data science to enhance your practical skills and knowledge.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find an open-source project that you are interested in.
- Create a GitHub account and fork the project.
- Make changes to the code and submit a pull request.
- Review code from other contributors and provide feedback.
Solve Data Analysis Problems
Show steps
Challenges students to apply data analysis techniques to solve real-world problems, strengthening their skills.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Collect data from a variety of sources.
- Clean and prepare the data for analysis.
- Apply appropriate data analysis techniques to extract insights.
- Interpret the results and draw conclusions.
Create a Comprehensive Course Summary
Show steps
Helps students organize and reinforce their learning by creating a centralized repository of course materials.
Browse courses on
Computational Social Science
Show steps
Show steps
Show steps
- Review lecture notes, textbooks, and other course materials.
- Summarize key concepts and insights in a structured format.
- Include relevant examples, diagrams, and elaborations.
Create a Course Summary
Show steps
Consolidate and organize your course materials for effective review and retention.
Show steps
Show steps
Show steps
- Gather all of your course materials, including notes, assignments, and quizzes.
- Organize the materials into a logical order.
- Create a summary of each lecture or module.
- Highlight the key concepts and ideas.
Develop a Computational Social Science Research Proposal
Show steps
Encourages students to apply course concepts to develop a proposal for their own computational social science research project.
Browse courses on
Computational Social Science
Show steps
Show steps
Show steps
- Review relevant literature and gather data.
- Identify a research topic and formulate a hypothesis.
- Design a data analysis plan.
- Write a research proposal outlining your research plan.
Career center
Learners who complete Computational Social Science Methods will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists analyze and interpret large datasets to extract meaningful insights and patterns. This course in Computational Social Science Methods would be particularly helpful for aspiring Data Scientists, as it provides a solid foundation in the scientific method and the use of computational tools for data analysis. The modules on data science and machine learning will be especially relevant to this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course in Computational Social Science Methods would be helpful for aspiring Machine Learning Engineers, as it provides an overview of the basic concepts behind machine learning and how it is used in various applications. The module on machine learning and AI will be particularly relevant to this role.
Social Media Manager
Social Media Manager
Social Media Managers plan and execute social media campaigns to promote products or services. This course in Computational Social Science Methods would be helpful for aspiring Social Media Managers, as it provides an overview of the current opportunities and reach of computational social science. The modules on social networks and computer simulations will be particularly relevant to this role.
User Experience (UX) Researcher
User Experience (UX) Researcher
UX Researchers study how users interact with products and services to improve the user experience. This course in Computational Social Science Methods would be helpful for aspiring UX Researchers, as it provides an overview of the scientific method and the use of computational tools for data analysis. The modules on data science and machine learning will be particularly relevant to this role.
Business Analyst
Business Analyst
Business Analysts analyze business data to identify opportunities and solve problems. This course in Computational Social Science Methods would be helpful for aspiring Business Analysts, as it provides a solid foundation in the scientific method and the use of computational tools for data analysis. The modules on data science and machine learning will be particularly relevant to this role.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course in Computational Social Science Methods would be helpful for aspiring Quantitative Analysts, as it provides a solid foundation in the scientific method and the use of computational tools for data analysis. The modules on data science and machine learning will be particularly relevant to this role.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course in Computational Social Science Methods would be helpful for aspiring Operations Research Analysts, as it provides a solid foundation in the scientific method and the use of computational tools for data analysis. The modules on data science and machine learning will be particularly relevant to this role.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course in Computational Social Science Methods would be helpful for aspiring Software Engineers, as it provides an overview of the current opportunities and reach of computational social science. The modules on data science and machine learning will be particularly relevant to this role.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and databases. This course in Computational Social Science Methods would be helpful for aspiring Data Engineers, as it provides an overview of the current opportunities and reach of computational social science. The modules on data science and machine learning will be particularly relevant to this role.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course in Computational Social Science Methods would be helpful for aspiring Statisticians, as it provides a solid foundation in the scientific method and the use of computational tools for data analysis. The modules on data science and machine learning will be particularly relevant to this role.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about markets and consumers to help businesses make informed decisions. This course in Computational Social Science Methods would be helpful for aspiring Market Researchers, as it provides an overview of the current opportunities and reach of computational social science. The modules on data science and machine learning will be particularly relevant to this role.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and spread of diseases. This course in Computational Social Science Methods would be helpful for aspiring Epidemiologists, as it provides an overview of the current opportunities and reach of computational social science. The modules on data science and machine learning will be particularly relevant to this role.
Public Health Analyst
Public Health Analyst
Public Health Analysts collect and analyze data to identify and address public health problems. This course in Computational Social Science Methods would be helpful for aspiring Public Health Analysts, as it provides an overview of the current opportunities and reach of computational social science. The modules on data science and machine learning will be particularly relevant to this role.
Sociologist
Sociologist
Sociologists study human societies and their interactions. This course in Computational Social Science Methods would be helpful for aspiring Sociologists, as it provides an overview of the current opportunities and reach of computational social science. The modules on social networks and computer simulations will be particularly relevant to this role.
Political Scientist
Political Scientist
Political Scientists study politics and government. This course in Computational Social Science Methods would be helpful for aspiring Political Scientists, as it provides an overview of the current opportunities and reach of computational social science. The modules on social networks and computer simulations will be particularly relevant to this role.
For more career information including salaries, visit:
OpenCourser.com/course/j8w4c8/computational
Reading list
We've selected 15 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Computational Social Science Methods.
Is helpful as a background reference and provides a conceptual framework for computational social science (CSS). It introduces key concepts and methods used in CSS, and discusses the potential of CSS to advance our understanding of social phenomena.
Provides an accessible introduction to causal inference, a statistical technique used in CSS to determine cause-and-effect relationships. It offers a comprehensive overview of the subject, with practical examples and exercises.
Save
This comprehensive book covers the principles and applications of forecasting, including time series analysis and econometric models. It provides insights into various forecasting techniques, making it a valuable reference for practitioners and researchers.
Classic text on social network analysis, providing a comprehensive overview of the field. It covers topics such as network measures, visualization techniques, and applications in various disciplines.
Shows how to use machine learning techniques to solve social problems, and covers a range of topics, including natural language processing, computer vision, and social network analysis. It also includes case studies and examples of how machine learning has been used to address issues such as healthcare, education, and disaster relief.
Save
Introduces the fundamental concepts of social and economic networks, covering topics such as graph theory, centrality measures, and network formation. It provides a solid understanding of network analysis and its applications in various fields.
Introduces agent-based modeling as a computational method for simulating social systems. It covers topics such as model design, calibration, and validation, providing a hands-on guide to building and analyzing agent-based models.
Classic reference on social network analysis, which key technique used in CSS. It covers a range of topics, including network theory, data collection, and analysis methods. It valuable resource for researchers and practitioners who want to learn more about social network analysis.
Provides a comprehensive overview of agent-based modeling, which technique used to simulate social systems. It covers a range of topics, including model design, data collection, and analysis methods. It valuable resource for researchers who want to use agent-based modeling to study social phenomena.
Provides a practical introduction to data science for business professionals. It covers topics such as data collection, cleaning, analysis, and visualization, with a focus on real-world applications.
Beginner-friendly introduction to machine learning, and covers a range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for researchers and practitioners who want to learn more about machine learning.
Provides an accessible introduction to deep learning for natural language processing tasks. It covers topics such as neural networks, word embeddings, and sequence models.
Save
Provides a comprehensive overview of the field of network science, covering topics such as network structure, dynamics, and applications in various disciplines.
Explores the social implications of computer-mediated communication. It covers topics such as online communities, social networks, and the impact of new technologies on social behavior.
Save
Provides an overview of the field of digital sociology, exploring the use of digital data and methods to study social phenomena. It covers topics such as social media analysis, big data, and digital inequality.
For more information about how these books relate to this course, visit:
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Effort
4 weeks of study, 2-5 hours/week
Level
Introductory
Via
Coursera
Institution
University of California, Davis
Instructor
Martin Hilbert
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Computational Social Science. It's comprised of five courses:
- Social Network Analysis
- Computational Social Science Methods (viewing)
- Big Data, Artificial Intelligence, and Ethics
- Computational Social Science Capstone Project
- Computer Simulations
Good to know
Explores the emerging field of computational social science, which combines social science with computational techniques to gain insights into human behavior
Taught by a team of experienced professors from UC Berkeley, UC Riverside, UC San Diego, and UC Merced, who are recognized for their research in computational social science
Presents real-world examples of how computational social science is applied in fields such as data science, machine learning, and social network analysis
Suitable for students with a background in social science, computer science, or a related field who are interested in learning about the latest advances in computational social science
Requires students to engage with interactive materials and participate in online discussions
Assumes that students have a basic understanding of statistics and research methods
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