Predictive Analytics using Networked Data in R

Starting at USD 39.00
4 hours duration

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

This course offers a comprehensive understanding of state-of-the-art predictive analytics using networked data in R. The primary objective of network analytics is to predict the classification of network nodes, such as identifying churners, fraudsters, defaulters, and more. To achieve this, we explore how to effectively utilize information from the network and its underlying structure in a predictive manner. One key concept we introduce is featurization, which involves incorporating network features alongside non-network features to enhance the performance of analytical models. Throughout the course, you will utilize the igraph package to create and label a customer network in a churn scenario, while also gaining a solid foundation in network learning. Additionally, we delve into the concepts of homophily, dyadicity, and heterophily, and demonstrate how these can provide valuable exploratory insights within your network. Leveraging the functionality of the igraph package, you will learn how to compute various network features, including both node-centric and neighbor-based features. Moreover, we explore the application of the Google PageRank algorithm to calculate network features and empirically validate their predictive capabilities. Finally, we guide you through the process of generating a flat dataset from the network and analyzing it using logistic regression and random forests. By the end of this course, you will have acquired the skills and knowledge necessary to perform advanced predictive analytics on networked data, enabling you to make informed decisions and gain valuable insights from complex network structures.

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DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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