Associate Professor
Applied Social Network Analysis in Python
What You'll Learn
- Represent and manipulate networked data using the NetworkX library
- Analyze the connectivity of a network
- Measure the importance or centrality of a node in a network
- Predict the evolution of networks over time
4 Modules
24 Hours
6 hrs per module (approx.)
Rating
About Applied Social Network Analysis in Python
This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem.
This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
Skills You'll Gain
- Network Analysis
- Python (Programming Language)
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Applied Social Network Analysis in Python introduces learners to modeling and analyzing networks using Python and the NetworkX library. You will explore why networks are useful representations, examine connectivity and robustness, measure node importance, and study how networks evolve. The course emphasizes hands-on labs and real-world applications of network analysis techniques.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Why Study Networks and Basics on NetworkX
- Reading: Syllabus
- Reading: Help us learn more about you!
- Video: Networks: Definition and Why We Study Them
- Video: Network Definition and Vocabulary
- Video: Node and Edge Attributes
- Video: Bipartite Graphs
- Reading: Notice for Auditing Learners: Assignment Submission
- Ungraded Lab: Loading Graphs in NetworkX
- Video: TA Demonstration: Loading Graphs in NetworkX
- Ungraded Lab: Assignment 1
- Graded: Module 1 Quiz
- Graded: Assignment 1 Submission
Module 2: Network Connectivity
- Video: Clustering Coefficient
- Video: Distance Measures
- Video: Connected Components
- Video: Network Robustness
- Ungraded Lab: Simple Network Visualizations in NetworkX
- Video: TA Demonstration: Simple Network Visualizations in NetworkX
- Ungraded Lab: Assignment 2
- Graded: Module 2 Quiz
- Graded: Assignment 2 Submission
Module 3: Influence Measures and Network Centralization
- Video: Degree and Closeness Centrality
- Video: Betweenness Centrality
- Video: Basic Page Rank
- Video: Scaled Page Rank
- Video: Hubs and Authorities
- Video: Centrality Examples
- Discussion Prompt: PageRank and Centrality in a real-life network
- Ungraded Lab: Assignment 3
- Graded: Module 3 Quiz
- Graded: Assignment 3 Submission
Module 4: Network Evolution
- Video: Preferential Attachment Model
- Reading: Power Laws and Rich-Get-Richer Phenomena (Optional)
- Video: Small World Networks
- Video: Link Prediction
- Ungraded Lab: Extracting Features from Graphs
- Reading: The Small-World Phenomenon (Optional)
- Ungraded Lab: Assignment 4
- Reading: Post-Course Survey
- Reading: Keep Learning with Michigan Online!
- Graded: Module 4 Quiz
- Graded: Assignment 4 Submission
Grading Policy
There are four quizzes worth 20% of your final grade, three programming assignments, each worth 18%, and one final programming assignment worth 26% of your final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Some related experience required
