Applied Social Network Analysis in Python
Master network analysis using NetworkX to analyze connectivity, measure node importance, and predict network evolution in Python.
Master network analysis using NetworkX to analyze connectivity, measure node importance, and predict network evolution in Python.
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 Applied Data Science with Python 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.
4.6
(2,699 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Represent and analyze complex networks using NetworkX
Measure network connectivity and robustness
Calculate node centrality and importance metrics
Predict network evolution and link formation
Apply network analysis to real-world datasets
Skills you'll gain
This course includes:
3.7 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces learners to network analysis using the NetworkX library in Python. Starting with fundamental concepts of network modeling and analysis, the curriculum progresses through network connectivity, centrality measures, and network evolution. Students learn to represent and manipulate networked data, analyze network robustness, measure node importance, and predict network changes over time. The course combines theoretical understanding with practical applications through hands-on programming assignments and real-world examples.
Why Study Networks and Basics on NetworkX
Module 1 · 7 Hours to complete
Network Connectivity
Module 2 · 5 Hours to complete
Influence Measures and Network Centralization
Module 3 · 5 Hours to complete
Network Evolution
Module 4 · 8 Hours to complete
Fee Structure
Instructor
Assistant Professor
Daniel Romero is an Assistant Professor in the School of Information at the University of Michigan, where his work explores the dynamics of Social and Information Networks. His research delves into the mechanisms that govern the evolution of networks, how information spreads across these networks, and how users interact with each other in online environments.
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4.6 course rating
2,699 ratings
Frequently asked questions
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