Overview
Context
The aim of network analytics is to predict to which class a network node belongs, such as churner or not, fraudster or not, defaulter or not, etc.
To accomplish this, we discuss how to leverage information from the network and its underlying structure in a predictive way. More specifically, we introduce the idea of featurization such that network features can be added to non-network features as such boosting the performance of any resulting analytical model.
In this Predictive Analytics using Networked Data in R course at Data Camp, you will use the igraph package to generate and label a network of customers in a churn setting and learn about the foundations of network learning.
Then, you will learn about homophily, dyadicity and heterophilicty, and how these can be used to get key exploratory insights in your network.
Next, you will use the functionality of the igraph package to compute various network features to calculate both node-centric as well as neighbor based network features.
Furthermore, you will use the Google PageRank algorithm to compute network features and empirically validate their predictive power. Finally, we teach you how to generate a flat dataset from the network and analyze it using logistic regression and random forests.
Programme Structure
Chapters
- Homophily
- Network Featurization
- Putting it all together
Key information
Duration
- Part-time
- 1 days
Start dates & application deadlines
PREREQUISITES
Network Analysis in RSupervised Learning in R: ClassificationLanguage
Delivered
Disciplines
Data Science & Big Data Software Engineering Data Analytics View 548 other Short Courses in Software Engineering in United StatesAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
PREREQUISITES
Network Analysis in R
Supervised Learning in R: Classification
Tuition Fee
-
International
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days. -
National
FreeTuition FeeBased on the tuition of 0 USD for the full programme during 1 days.
Basic Access: Free; Premium (for individuals): $12.42 per month billed annually; Teams: $25 per month billed annually; Enterprise: Contact sales for pricing