Applied Social Network Analysis in Python

The learner will be introduced to network analysis through tutorials using the NetworkX library in this course. Module One of this course introduces you to various types of networks in the real world and why they are studied. You will learn about the fundamental elements of networks as well as the various types of networks. You'll also learn how to use the NetworkX library to represent and manipulate networked data.

In Module Two, you'll learn how to analyze network connectivity using distance, reachability, and redundancy of paths between nodes. You will practice using NetworkX to compute measures of connectivity of an email communication network among employees of a mid-size manufacturing company in this assignment.

In Module Three, you'll look at different ways to assess a node's importance or centrality in a network, such as Degree, Closeness, and Betweenness centrality, Page Rank, and Hubs and Authorities. You'll learn about the assumptions that each measure makes, the algorithms that can be used to compute them, and the various functions available to measure centrality on NetworkX.

In Module Four, you will investigate the evolution of networks over time, as well as the various models that generate realistic networks, such as the Preferential Attachment Model and Small World Networks. You will also investigate the link prediction problem, where you will learn useful features for predicting whether two disconnected nodes will be connected in the future.

This course offers:

• Flexible deadlines: Reset deadlines based on your availability.
• Shareable certificate: Get a Certificate when you complete
• 100% online
• Course 5 out of 5 in the Applied Data Science Specialization with Python
• Intermediate level
• Approx. 29 hours to complete

Course ratings: 4.7/5

Enroll here: https://www.coursera.org/learn/python-social-network-analysis