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Introduction to Network Analysis in Python

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4 hours
4,100 XP
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Course Description

From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere—and knowing how to analyze them will open up a new world of possibilities for you as a data scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to look at your data with a fresh perspective.
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  1. 1

    Introduction to networks


    In this chapter, you'll be introduced to fundamental concepts in network analytics while exploring a real-world Twitter network dataset. You'll also learn about NetworkX, a library that allows you to manipulate, analyze, and model graph data. You'll learn about the different types of graphs and how to rationally visualize them.

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    Introduction to Networks
    50 xp
    What is a network?
    50 xp
    Basics of NetworkX API, using Twitter network
    50 xp
    Basic drawing of a network using NetworkX
    100 xp
    Queries on a graph
    100 xp
    Types of graphs
    50 xp
    Checking the un/directed status of a graph
    50 xp
    Specifying a weight on edges
    100 xp
    Checking whether there are self-loops in the graph
    100 xp
    Network visualization
    50 xp
    Visualizing using Matrix plots
    100 xp
    Visualizing using Circos plots
    100 xp
    Visualizing using Arc plots
    100 xp
  2. 2

    Important nodes

    You'll learn about ways to identify nodes that are important in a network. In doing so, you'll be introduced to more advanced concepts in network analysis as well as the basics of path-finding algorithms. The chapter concludes with a deep dive into the Twitter network dataset which will reinforce the concepts you've learned, such as degree centrality and betweenness centrality.

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  3. 3


    This chapter is all about finding interesting structures within network data. You'll learn about essential concepts such as cliques, communities, and subgraphs, which will leverage all of the skills you acquired in Chapter 2. By the end of this chapter, you'll be ready to apply the concepts you've learned to a real-world case study.

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  4. 4

    Bringing it all together

    In this final chapter of the course, you'll consolidate everything you've learned through an in-depth case study of GitHub collaborator network data. This is a great example of real-world social network data, and your newly acquired skills will be fully tested. By the end of this chapter, you'll have developed your very own recommendation system to connect GitHub users who should collaborate together.

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Twitter networkGitHub users


Collaborator's avatar
Hugo Bowne-Anderson
Collaborator's avatar
Yashas Roy
Eric Ma HeadshotEric Ma

Data Carpentry instructor and author of nxviz package

Eric uses code to solve big biological data problems at MIT. His tools of choice are: deep learning, network analysis, non-parametric and Bayesian statistics. He has domain expertise in the life sciences: molecular biology, microbiology, genetics and genomics, and a bit of ecology. He has given workshops on Network Analysis at PyCon, PyData, ODSC and beyond!
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