Course
Introduction to Network Analysis in Python
IntermediateSkill Level
Updated 05/2026PythonProbability & Statistics4 hr14 videos50 Exercises4,100 XP74,029Statement of Accomplishment
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Prerequisites
Python Toolbox1
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.
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.
3
Structures
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.
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.
Introduction to Network Analysis in Python
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FAQs
Which Python library does this course use for network analysis?
The course uses the NetworkX library throughout all four chapters for manipulating, analyzing, and modeling graph data.
What real-world datasets are explored in this course?
You analyze a Twitter network dataset for most of the course, then work with GitHub collaborator network data in the final chapter case study.
What network concepts will I learn?
You cover graph types, degree centrality, betweenness centrality, path-finding algorithms, cliques, communities, and subgraphs, building from fundamentals to advanced structures.
Does this course include a hands-on project?
Yes. The final chapter is a case study where you build a recommendation system that suggests GitHub users who should collaborate, applying all skills from the course.
What Python skills do I need before starting?
You need Intermediate Python, Introduction to Functions in Python, and Python Toolbox. No prior network analysis or graph theory knowledge is required.
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