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Applied Social Network Analysis in Python
  • Level Professional
  • المدة 26 ساعات hours
  • الطبع بواسطة University of Michigan
  • Offered by

عن

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.

الوحدات

Module 1: Why Study Networks and Basics on NetworkX

    • 5 Videos
    • 3 Readings
    • 2 Lab
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2 Labs

  • Creating and Manipulating Graphs with NetworkX
  • Loading Graphs in NetworkX

5 Videos

  • Networks: Definition and Why We Study Them
  • Network Definition and Vocabulary
  • Node and Edge Attributes
  • Bipartite Graphs
  • TA Demonstration: Loading Graphs in NetworkX

3 Readings

  • Course Syllabus
  • Help us learn more about you!
  • Notice for Auditing Learners: Assignment Submission

Week 1 - Assignments

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Assignment 1

1 Assignment

  • Module 1 Quiz

Module 2: Network Connectivity

    • 5 Videos
    • 1 Lab
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1 Labs

  • Simple Network Visualizations in NetworkX

5 Videos

  • Clustering Coefficient
  • Distance Measures
  • Connected Components
  • Network Robustness
  • TA Demonstration: Simple Network Visualizations in NetworkX

Week 2 - Assignments

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Assignment 2

1 Assignment

  • Module 2 Quiz

Module 3: Influence Measures and Network Centralization

    • 6 Videos
    • 1 Discussion
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1 Discussions

  • PageRank and Centrality in a real-life network

6 Videos

  • Degree and Closeness Centrality
  • Betweenness Centrality
  • Basic Page Rank
  • Scaled Page Rank
  • Hubs and Authorities
  • Centrality Examples

Week 3 - Assignments

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Assignment 3

1 Assignment

  • Module 3 Quiz

Module 4: Applications

    • 3 Videos
    • 2 Readings
    • 1 Lab
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1 Labs

  • Extracting Features from Graphs

3 Videos

  • Preferential Attachment Model
  • Small World Networks
  • Link Prediction

2 Readings

  • Power Laws and Rich-Get-Richer Phenomena (Optional)
  • The Small-World Phenomenon (Optional)

Week 4 - Assignments

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Assignment 4

1 Assignment

  • Module 4 Quiz

Post-Course Survey

    • 3 Readings
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3 Readings

  • Post-Course Survey
  • Keep Learning with Michigan Online!
  • Special invitation from the MADS program director

Auto Summary

"Applied Social Network Analysis in Python" is a professional-level Data Science & AI course offered by Coursera. Taught through engaging tutorials using the NetworkX library, it covers network analysis fundamentals, connectivity, centrality, and network evolution. Ideal for those with prior Python and data science knowledge, the course spans 1560 minutes and is available via a Starter subscription. Perfect for data enthusiasts aiming to deepen their network analysis skills.

Daniel Romero
Instructor

Daniel Romero