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Predictive Analytics using Networked Data in R

IntermediateSkill Level
4.7+
29 reviews
Updated 09/2020
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
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RProbability & Statistics4 hr14 videos56 Exercises4,300 XP4,749Statement of Accomplishment

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Course Description

In this course, you will learn to perform state-of-the art predictive analytics using networked data in R. 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 course, 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.

Prerequisites

Network Analysis in RSupervised Learning in R: Classification
1

Introduction, networks and labelled networks

In this chapter you will be introduced to labelled networks, network learning and the challanges that can arise.
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2

Homophily

3

Network Featurization

4

Putting it all together

Predictive Analytics using Networked Data in R
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*4.7
from 29 reviews
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  • Nour
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  • Kameron
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  • Tung
    4 months ago

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  • Maddie
    6 months ago

  • Lambert
    7 months ago

  • Mariel
    8 months ago

Nour

Lambert

Mariel

FAQs

Is this course suitable for beginners?

No. This course is aimed at Intermediate learners with work experience in R. We recommend first taking the "Network Analysis in R" and "Network Analysis in R" courses.

What topics does the course cover?

This course covers topics such as labelled networks, network learning, homophily, network featurization, and how to generate a flat dataset from the network for analytics.

What tools will I learn to use?

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. You will also use the Google PageRank algorithm to compute network features.

Will I receive a certificate at the end of the course?

Yes, if you successfully complete all the course requirements, you will receive a certificate of completion.

Who will benefit from this course?

Professionals in data science, artificial intelligence, machine learning, network analysis, and predictive analytics roles would benefit from taking this course.

Does the course contain any exercises?

Yes, the course contains several hands-on exercises that require you to apply the concepts you have learned to a real-life scenario.

Are there any prerequisites for this course?

Yes, familiarity with R and taking the "Network Analysis in R" and "Network Analysis in R" courses is recommended.

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