Data Community DC and District Data Labs are excited to be hosting another Social Network Analysis with Python workshop on Saturday August 15th where you can learn how to use Python to construct and analyze a social network, compute cardinality, traverse and query graphs, compute clusters, and create visualizations. For more info and to sign up, go to the DDL course page. Register before August 1st for an early bird discount!
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
What You Will Learn
In this course we will construct a social network from email communications using Python. We will learn analyses that compute cardinality, as well as traversal and querying techniques on the graph, and even compute clusters to detect community. Besides learning the basics of graph theory, we will also make predictions and create visualizations from our graphs so that we can easily harness social networks in larger data products.
The workshop will cover the following topics:
- Email Mbox format for conducting analysis
- Reading emails with Python
- Creating a graph using NetworkX
- Serializing and deserializing NetworkX graphs
- An introduction to Graph theory
- Finding strong ties through link weighting
- Computing centrality and key players (celebrities)
- Finding communities through clustering techniques
- Visualizing graphs with matplotlib
Upon completion of the course, you will understand how to conduct graph analyses on social networks, as well as have built a library for analyses on a social network constructed from email communications!
Instructor: Benjamin Bengfort
Benjamin is an experienced Data Scientist and Python developer who has worked in military, industry, and academia for the past eight years. He is currently pursuing his PhD in Computer Science at The University of Maryland, College Park, doing research in Metacognition and Active Logic. He is also a Data Scientist at Cobrain Company in Bethesda, MD where he builds data products including recommender systems and classifier models. He holds a Masters degree from North Dakota State University where he taught undergraduate Computer Science courses. He is also adjunct faculty at Georgetown University where he teaches Data Science and Analytics.