After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.
Prerequisites
Curriculum
Course outline
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.
- Introduction to Networks50 XP
- What is a network?50 XP
- Basics of NetworkX API, using Twitter network50 XP
- Basic drawing of a network using NetworkX100 XP
- Queries on a graph100 XP
- Types of graphs50 XP
- Checking the un/directed status of a graph50 XP
- Specifying a weight on edges100 XP
- Checking whether there are self-loops in the graph100 XP
- Network visualization50 XP
- Visualizing using Matrix plots100 XP
- Visualizing using Circos plots100 XP
- Visualizing using Arc plots100 XP
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
Course
Complete
