Social Network Analysis
Master advanced network analysis techniques using R for analyzing social structures and relationships.
Master advanced network analysis techniques using R for analyzing social structures and relationships.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Social Computing Specialization or Social Media Analytics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
English
Not specified
Powered by
What you'll learn
Calculate and interpret key centrality measures
Apply statistical models to analyze network relationships
Understand foundational social theories
Construct and analyze various network types
Implement ERGM and SAOM models
Conduct hypothesis testing with empirical data
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This comprehensive course explores advanced social network analysis, combining theoretical foundations with practical applications. Students learn to analyze complex social structures using statistical methods and R programming, focusing on centrality measures, graph theory, and social forces. The curriculum covers exponential random graph models (ERGM) and stochastic actor-oriented models (SAOM) using tools like 'statnet' and 'RSiena', preparing students for advanced network analytics.
Course Introduction
Module 1 · 14 Minutes to complete
Graph Theory and Centrality Measures
Module 2 · 4 Hours to complete
Centralization and Social Theory
Module 3 · 4 Hours to complete
Network Statistical Models
Module 4 · 3 Hours to complete
Fee Structure
Instructor
Leading Innovator in Social Network Analysis and Artificial Intelligence
Ian McCulloh is an esteemed associate professor at Johns Hopkins University, holding joint appointments in the Bloomberg School of Public Health and the Whiting School of Engineering. With a Ph.D. in medieval European history and global medieval history, he has shifted his focus to social neuroscience, social network analysis, and artificial intelligence (AI). Ian's extensive research includes over 100 peer-reviewed papers and several influential books, such as Social Network Analysis with Applications and ISIS in Iraq: Understanding the Social and Psychological Foundations of Terror. He is also the founder of the Brain Rise Foundation, which aims to enhance treatment for substance abuse and related disorders through advanced neuroscience and AI research. His innovative projects include developing immersive digital experiences that explore historical contexts and cultural interactions, as well as leading initiatives to improve clinical decision-making through AI in organ transplantation. A retired Lieutenant Colonel from the U.S. Army with a rich background in social network analysis for military applications, Ian continues to contribute to academia and public health through his pioneering work in technology and education.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.