Network Analysis with R

This course will show you how to work with network data. You will use the jgraph package to create networks with edgelists or adjacency maters. Learn how to plot networks and their attributes. Next, you'll learn how to identify important vertices using measures like betweenness and degree. Next, you will learn about network structures such as cliques or triangles. Next, you'll learn how to identify special relationships between vertices by using metrics like assortativity. Learn how to create interactive network maps with threejs.

This course will show you how to use networked information to perform advanced predictive analysis. We will be discussing how to use the network's structure and information in a predictive way. We also introduce featurization which allows network features and other features to be added. This improves the performance of any analytic model. This course will show you how to use igraph to label and create a network of customers in a churn environment. The foundations of network learning will also be covered. Next, you will learn about heterophily and dyadicity and how they can help you gain important insights into your network. Next, you will use the igraph package functionality for computing network features. This includes both neighbor-based and node centric network features. The Google PageRank algorithm will be used to compute and validate network features. We will also demonstrate how to create a flat data set using the network, and then analyze it with logistic regression or random forest.

If you are interested in learning more about information networks, social network, and the neural networks within our brains, network science is essential. This project will demonstrate network analysis using different R packages, such as dplyr, ggplot2, visNetwork and igraph. Your role as Interpol Analyst is to investigate the terrorist network behind the Madrid train bombing in 2004. You will be able to create interactive visualizations and analyze any network.

In this course on network analysis, we will explore how these concepts can be applied to real-world data sets. Through a series of case studies, we will build on your existing knowledge and show you how to handle data in academic and industrial settings. We will cover various challenges you may encounter in computing and visualization and provide solutions to overcome them. While we will primarily focus on using igraph, we will also discuss other visualization libraries that can enhance your online visualizations. This course is ideal for individuals who want to improve their data analysis skills in R programming. By the end of the course, you will have a solid understanding of network analysis techniques and be equipped to tackle complex data analysis projects using R.