Capstone: Analyzing (Social) Network Data
This course is part of Object Oriented Java Programming: Data Structures and Beyond.
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 Object Oriented Java Programming: Data Structures and Beyond 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.
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What you'll learn
Implement graph algorithms to analyze complex social network data
Design efficient data structures for representing and manipulating network information
Evaluate algorithm efficiency using asymptotic analysis
Identify influential members and sub-communities within a social network
Visualize network data to reveal patterns and relationships
Communicate technical findings through written reports and oral presentations
Skills you'll gain
This course includes:
1.87 Hours PreRecorded video
6 assignments
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There are 6 modules in this course
This capstone project combines skills from across the Object-Oriented Java Programming specialization to analyze social network data using graph algorithms and data structures. Students begin by representing social network data as a graph and implementing algorithms for extracting "egonets" (personal networks) and identifying strongly connected components. After this warm-up phase, students define their own project scope, identifying specific questions about social networks they wish to explore and researching appropriate algorithms. The course follows a structured approach with students first implementing a smaller-scale "mini-project" for peer feedback before tackling a more complex problem. Throughout the project, students apply their knowledge of algorithm analysis, object-oriented design, and data structures to optimize their solutions. The course culminates with students creating both written and oral presentations of their work, demonstrating their ability to analyze, implement, and communicate technical solutions to real-world problems. This project-based approach allows students to showcase their programming skills while gaining practical experience with social network analysis.
Introduction and Warm up
Module 1 · 6 Hours to complete
Project Definition and Scope
Module 2 · 2 Hours to complete
Capstone Implementation: Mini-project
Module 3 · 2 Hours to complete
Capstone Implementation: Full project checkpoint
Module 4 · 1 Hours to complete
Capstone Implementation: Full project final deadline
Module 5 · 3 Hours to complete
Capstone oral report
Module 6 · 2 Hours to complete
Instructors
5 Courses
Distinguished Computer Science Educator and Educational Innovation Pioneer
Dr. Leo Porter serves as a Professor of Computer Science at UC San Diego, where he co-founded the Computing Education Research Laboratory focused on understanding how students learn computing and creating inclusive learning environments. His journey includes service as a surface warfare officer in the U.S. Navy's Pacific fleet and Operation Iraqi Freedom before earning his M.S. and Ph.D. in computer science from UC San Diego in 2007. His groundbreaking research in computer science education, particularly on Peer Instruction and active learning pedagogies, has earned numerous accolades, including Best Paper Awards at SIGCSE and the International Computing Education Research Conference. Recently, he co-authored "Learn AI-Assisted Python Programming" with Daniel Zingaro, addressing the integration of AI tools in programming education. His research spans computer architecture, educational technology, and student learning assessment, with particular emphasis on using data-driven approaches to predict student outcomes and identify critical course concepts. As a Distinguished Member of the ACM, he has influenced over 500,000 learners through popular Coursera and edX courses while maintaining active research in both computer science education and computer architecture.
Champion of Inclusive Computer Science Education
Christine Alvarado serves as Associate Teaching Professor in Computer Science and Engineering at UC San Diego and Associate Dean for the Division of Undergraduate Education. Her distinguished career combines technical expertise with a passionate commitment to diversifying computer science education. After earning her Ph.D. from MIT in 2004, she began her academic career at Harvey Mudd College before joining UCSD in 2012. Her innovative work includes founding the CSE Early Research Scholars Program, which has engaged over 339 early undergraduates in computing research, with significant participation from women, non-binary students, and underrepresented racial groups
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