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Martin Hilbert
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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What's inside
Syllabus
Getting Started and Milestone 1
For this milestone, you will again web scrape videos from two YouTube channels. You will be assigned two channels to scrape. In contrast to the previous version of this exercise, you will NOT scrape the featured videos of the specified news channel, but the search results of the name of the news channel in combination with your name.
Read more
Syllabus
Getting Started and Milestone 1
For this milestone, you will again web scrape videos from two YouTube channels. You will be assigned two channels to scrape. In contrast to the previous version of this exercise, you will NOT scrape the featured videos of the specified news channel, but the search results of the name of the news channel in combination with your name.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches students how to collect, analyze, and interpret data in a social media context, which is critical in today's digital age
Provides hands-on experience with data scraping and analyzing, which are in-demand skills in various fields
Covers advanced topics such as natural language processing and agent-based simulations, which are cutting-edge techniques in computational social science
Taught by Martin Hilbert, an established researcher in the field of computational social science, which lends credibility to the course
Provides a comprehensive overview of computational social science, making it suitable for students with various backgrounds in social science, data science, and computer science
Requires some prior knowledge of social science concepts and data analysis techniques, which may not be suitable for complete beginners
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Reviews summary
Computational social science capstone project
According to learners, the Computational Social Science Capstone Project offers a highly valuable and practical culmination to the specialization. Students frequently highlight the opportunity to apply the skills learned in previous courses—web scraping, social network analysis (SNA), natural language processing (NLP), and agent-based simulations—to a comprehensive project. The hands-on nature of the milestones is seen as a major strength, providing real-world experience. While integrating these methods can be challenging, especially the simulation component, many find the effort worthwhile for developing a solid foundation in the field. Some reviews mention needing to spend significant time troubleshooting or supplementing with outside resources, reflecting the complexity of the subject matter.
Effective summary of the specialization.
"This capstone is an excellent way to wrap up the specialization and solidify understanding."
"Completing this project made me feel like I had truly gained a grasp of computational social science methods."
"It successfully demonstrated how all the courses in the specialization connect and are used in practice."
"A very well-designed final project that showcases the power of the methods learned."
Practical exercises provide valuable skills.
"Getting my hands dirty with Gephi, IBM Watson, and running simulations was crucial for learning."
"The focus on completing tangible milestones made the learning concrete and applicable."
"Building the project step-by-step gave me confidence in my ability to perform these tasks independently."
"This is not just theory; you actually build something, which is the best way to learn."
Apply diverse methods to a complex project.
"The project is a brilliant way to tie all the pieces together and apply scraping, SNA, NLP, and simulation."
"I appreciated how we took real-world data and applied all the computational methods covered in the specialization."
"Putting all the different tools and concepts from the previous courses into practice was incredibly valuable."
"It felt like a true capstone, allowing me to integrate everything I'd learned into one cohesive project."
Connecting all parts requires significant effort.
"While the integration is the point, making everything work together smoothly took more effort than anticipated."
"Connecting the output from one milestone to the input of the next had some tricky points that needed careful handling."
"You really need to understand how the different computational methods fit together for the project to succeed."
"It was challenging but rewarding to see how scraping data led to network analysis, then NLP, and finally simulation."
Agent-based modeling milestone is challenging.
"The agent-based simulation part was particularly difficult for me to grasp and implement correctly."
"Milestone 4 required the most troubleshooting and felt like a steep learning curve compared to the others."
"While interesting, the simulation segment was complex and could use more detailed guidance or examples."
"I struggled with getting the simulation model to run as expected; it was the main hurdle."
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 Capstone Project with these
activities:
Brush up on Python basics
Show steps
Refresh your memory on the basics of the Python programming language to prepare for the course's programming exercises.
Browse courses on
Python Basics
Show steps
-
Review the basic syntax and data types in Python.
-
Solve a few practice problems on Python basics.
Review Data Science for Dummies
Show steps
Review the fundamentals of data science to refresh your knowledge and strengthen your foundation for the course.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
-
Read through the first three chapters of the book.
-
Take notes on the key concepts and definitions.
-
Complete the practice exercises at the end of each chapter.
Tutorial: Using Gephi for Social Network Visualization
Show steps
Enhance understanding of network visualization techniques.
Browse courses on
Social Network Analysis
Show steps
-
Familiarize withGephi's user interface.
-
Import and explore network data in Gephi.
-
Apply different layout algorithms to visualize the network.
-
Identify key network metrics and visualize them.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Tutorial: Agent-Based Modeling with NetLogo
Show steps
Gain hands-on experience in agent-based modeling.
Browse courses on
Agent-Based Modeling
Show steps
-
Install and familiarize with the NetLogo environment.
-
Create a simple agent-based model in NetLogo.
-
Run the model and observe its behavior.
-
Modify the model parameters to explore different scenarios.
Follow a tutorial on machine learning
Show steps
Enhance your understanding of machine learning concepts through a structured tutorial, providing practical demonstrations and examples.
Browse courses on
Machine Learning
Show steps
-
Find a reputable tutorial on machine learning for beginners.
-
Follow the tutorial step-by-step, completing all the exercises and examples.
-
Take notes on the key concepts and algorithms discussed in the tutorial.
Exercise 2: Social Network Analysis of YouTube Channel Interactions
Show steps
Enhance understanding of network analysis concepts.
Browse courses on
Social Network Analysis
Show steps
-
Gather interaction data from the YouTube channels.
-
Construct a network representing the interactions.
-
Calculate network metrics to analyze the structure and dynamics of the network.
-
Identify influential users and communities within the network.
-
Visualize the network and infer insights from the analysis.
Participate in a study group
Show steps
Enhance your learning through collaboration and discussion with peers, solidifying your understanding of the course material.
Show steps
-
Find a study group with other course participants.
-
Meet regularly to discuss course topics, share insights, and work on assignments together.
Exercise 1: NLP Sentiment Analysis on YouTube Comments
Show steps
Reinforce NLP concepts by analyzing sentiment from YouTube comments.
Show steps
-
Install required Python NLP libraries.
-
Gather the comment data from the YouTube videos.
-
Clean and prepare the comment text for analysis.
-
Apply sentiment analysis techniques to extract sentiment scores.
-
Visualize and interpret the sentiment analysis results.
Project: Analyze Social Media Campaign using Computational Techniques
Show steps
Apply computational social science techniques to gain insights from a real-world social media campaign.
Show steps
-
Identify a social media campaign to analyze.
-
Collect and clean the relevant social media data.
-
Extract insights using NLP, SNA, or agent-based modeling techniques.
-
Create visualizations and reports to present the findings.
-
Evaluate the effectiveness of the campaign and identify areas for improvement.
Volunteer at a local organization
Show steps
Apply your skills in a real-world setting, gaining practical experience and contributing to your community.
Show steps
-
Find a local organization that aligns with your interests.
-
Inquire about volunteer opportunities and responsibilities.
-
Commit to a regular volunteering schedule.
Contribute to an open-source project
Show steps
Engage with the open-source community, contribute to a project, and gain valuable experience in collaborative development.
Show steps
-
Find an open-source project that aligns with your skills and interests.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute.
-
Submit a pull request with your proposed changes.
Participate in a data science competition
Show steps
Challenge yourself by participating in a data science competition, putting your skills to the test and gaining valuable experience.
Show steps
-
Find a data science competition that aligns with your interests.
-
Form a team or participate individually.
-
Develop a solution to the competition problem.
-
Submit your solution and track your progress on the leaderboard.
Brush up on Python basics
Show steps
Refresh your memory on the basics of the Python programming language to prepare for the course's programming exercises.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review the basic syntax and data types in Python.
- Solve a few practice problems on Python basics.
Review Data Science for Dummies
Show steps
Review the fundamentals of data science to refresh your knowledge and strengthen your foundation for the course.
View
Artificial Intelligence For Dummies (For...
on Amazon
Show steps
Show steps
Show steps
- Read through the first three chapters of the book.
- Take notes on the key concepts and definitions.
- Complete the practice exercises at the end of each chapter.
Tutorial: Using Gephi for Social Network Visualization
Show steps
Enhance understanding of network visualization techniques.
Browse courses on
Social Network Analysis
Show steps
Show steps
Show steps
- Familiarize withGephi's user interface.
- Import and explore network data in Gephi.
- Apply different layout algorithms to visualize the network.
- Identify key network metrics and visualize them.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Tutorial: Agent-Based Modeling with NetLogo
Show steps
Gain hands-on experience in agent-based modeling.
Browse courses on
Agent-Based Modeling
Show steps
Show steps
Show steps
- Install and familiarize with the NetLogo environment.
- Create a simple agent-based model in NetLogo.
- Run the model and observe its behavior.
- Modify the model parameters to explore different scenarios.
Follow a tutorial on machine learning
Show steps
Enhance your understanding of machine learning concepts through a structured tutorial, providing practical demonstrations and examples.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a reputable tutorial on machine learning for beginners.
- Follow the tutorial step-by-step, completing all the exercises and examples.
- Take notes on the key concepts and algorithms discussed in the tutorial.
Exercise 2: Social Network Analysis of YouTube Channel Interactions
Show steps
Enhance understanding of network analysis concepts.
Browse courses on
Social Network Analysis
Show steps
Show steps
Show steps
- Gather interaction data from the YouTube channels.
- Construct a network representing the interactions.
- Calculate network metrics to analyze the structure and dynamics of the network.
- Identify influential users and communities within the network.
- Visualize the network and infer insights from the analysis.
Participate in a study group
Show steps
Enhance your learning through collaboration and discussion with peers, solidifying your understanding of the course material.
Show steps
Show steps
Show steps
- Find a study group with other course participants.
- Meet regularly to discuss course topics, share insights, and work on assignments together.
Exercise 1: NLP Sentiment Analysis on YouTube Comments
Show steps
Reinforce NLP concepts by analyzing sentiment from YouTube comments.
Show steps
Show steps
Show steps
- Install required Python NLP libraries.
- Gather the comment data from the YouTube videos.
- Clean and prepare the comment text for analysis.
- Apply sentiment analysis techniques to extract sentiment scores.
- Visualize and interpret the sentiment analysis results.
Project: Analyze Social Media Campaign using Computational Techniques
Show steps
Apply computational social science techniques to gain insights from a real-world social media campaign.
Show steps
Show steps
Show steps
- Identify a social media campaign to analyze.
- Collect and clean the relevant social media data.
- Extract insights using NLP, SNA, or agent-based modeling techniques.
- Create visualizations and reports to present the findings.
- Evaluate the effectiveness of the campaign and identify areas for improvement.
Volunteer at a local organization
Show steps
Apply your skills in a real-world setting, gaining practical experience and contributing to your community.
Show steps
Show steps
Show steps
- Find a local organization that aligns with your interests.
- Inquire about volunteer opportunities and responsibilities.
- Commit to a regular volunteering schedule.
Contribute to an open-source project
Show steps
Engage with the open-source community, contribute to a project, and gain valuable experience in collaborative development.
Show steps
Show steps
Show steps
- Find an open-source project that aligns with your skills and interests.
- Review the project's documentation and codebase.
- Identify an area where you can contribute.
- Submit a pull request with your proposed changes.
Participate in a data science competition
Show steps
Challenge yourself by participating in a data science competition, putting your skills to the test and gaining valuable experience.
Show steps
Show steps
Show steps
- Find a data science competition that aligns with your interests.
- Form a team or participate individually.
- Develop a solution to the competition problem.
- Submit your solution and track your progress on the leaderboard.
Career center
Learners who complete Computational Social Science Capstone Project will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop computational methods to extract insights from large datasets and visualize the results. Graduates of this course may develop their ability to draw insights from social media data, a valuable skill in this field. This course also teaches natural language processing, helping you to interact with unstructured data.
Computational Social Scientist
Computational Social Scientist
Computational Social Scientists develop research questions, collect data, and construct mathematical and computational models to study complex social phenomena. The combination of social science theory and technical skills covered in this course may prepare you to excel in this challenging and rewarding career.
Social Media Analyst
Social Media Analyst
Social Media Analysts research and analyze the impact of social media campaigns, and provide recommendations on how to improve engagement. This course covers web scraping, social network analysis, and natural language processing, all of which are foundational skills for success in this role.
Market Researcher
Market Researcher
Market Researchers gather and interpret data about consumer behavior and preferences. This course offers a comprehensive introduction to computational social science methods, which are increasingly used in market research to collect and analyze data.
Public Relations Specialist
Public Relations Specialist
Public Relations Specialists manage the public image of organizations and individuals. Graduates of this course may develop their skills in using social media data to monitor and analyze public opinion and develop effective communication strategies.
Communications Manager
Communications Manager
Communications Managers oversee all aspects of an organization's communication, including public relations, marketing, and social media. This course provides a foundation in computational social science methods, which can help you to understand and communicate complex information effectively.
Nonprofit Program Manager
Nonprofit Program Manager
Nonprofit Program Managers plan, implement, and evaluate programs that address social issues. The skills and knowledge covered in this course, such as data collection and analysis, may be valuable in developing and evaluating effective programs.
Policy Analyst
Policy Analyst
Policy Analysts research and analyze public policy issues, and provide recommendations to policymakers. Graduates of this course who have a strong understanding of computational social science methods may be able to contribute to the development of evidence-based policies.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how users interact with products and services, and provide recommendations on how to improve the user experience. This course may assist you in developing your skills in collecting and analyzing data to understand user needs.
Social Media Manager
Social Media Manager
Social Media Managers develop and implement social media strategies for organizations. This course may help you to develop your skills in using data to track and measure the success of social media campaigns.
Digital Marketing Analyst
Digital Marketing Analyst
Digital Marketing Analysts use data to understand the effectiveness of digital marketing campaigns. This course teaches data collection and analysis methods that are essential for success in this role.
Web Developer
Web Developer
Web Developers design and develop websites. This course may help you to develop your skills in using social media data to create and improve websites.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. This course may help to develop your skills in using data to improve the design and functionality of software systems.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. The skills covered in this course provide a solid foundation for success in data analysis.
Business Analyst
Business Analyst
Business Analysts apply analytical techniques to solve business problems and improve decision-making. This course provides a foundation in data collection and analysis methods that are essential for success in business analysis.
For more career information including salaries, visit:
OpenCourser.com/course/zfoqwn/computational
Reading list
We've selected six 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 Capstone Project.
Save
This classic text provides a solid foundation in social network analysis concepts and theories, enhancing the understanding gained in Milestone 2.
Delves into natural language processing, a crucial topic covered in Milestone 3. It provides a solid foundation for understanding NLP concepts and techniques.
Save
This practical guide equips learners with text mining techniques using R, providing valuable insights into Milestone 3's NLP component.
This introductory book offers a comprehensive overview of NLP concepts and techniques, providing a valuable foundation for Milestone 3.
This practical guide focuses on network analysis techniques and provides hands-on experience with Gephi, a software used in Milestone 2 of the course.
This user-friendly guide offers a thorough introduction to agent-based modeling, a key concept explored in Milestone 4.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zfoqwn/computational
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Effort
4 weeks of study, 2-5 hours/week
Level
Intermediate
Via
Coursera
Institution
University of California, Davis
Instructor
Martin Hilbert
Language
English
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This course belongs to a
collection
called
Computational Social Science. It's comprised of five courses:
- Social Network Analysis
- Computational Social Science Methods
- Big Data, Artificial Intelligence, and Ethics
- Computational Social Science Capstone Project (viewing)
- Computer Simulations
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