Fraud Analytics Using R & Microsoft Excel

Posted By: ELK1nG

Fraud Analytics Using R & Microsoft Excel
Last updated 6/2019
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.05 GB | Duration: 2h 22m

Learn how to Introduction to Fraud,Different Types of Fraud and Fraud Analytics & Importance

What you'll learn
Learn how to effectively work around pricing to find out answers to key questions related to business analysis.
Requirements
Students
Marketing professionals
Market Researchers
Product Managers
Any person running a business
Anyone interested in learning about fraud analytics
Description
Learn how to effectively work around pricing to find out answers to key questions related to business analysis. We are using sophisticated statistical tools like R and Tableau to analyze data.this training is a practical and a quantitative course which will help you learn fraud analytics with the perspective of a data scientist. The learner of this course will learn the most relevant techniques used in the real world by data analysts of companies around the world.The training includes the following;Introduction to FraudDifferent Types of FraudFraud Analytics & ImportanceTraditional Fraud Detection MethodFraud Detection – BIG Data ApproachFraud Detection, Prevention & AnalyticsCase Study : Credit Card Fraud Detection

Overview

Section 1: Introduction

Lecture 1 Introduction to Fraud

Section 2: Types of Fraud and Detail Fraud Analytics

Lecture 2 Types of Fraud

Lecture 3 Fraud Analytics

Lecture 4 Details of Fraud

Lecture 5 Traditional Fraud Detection Method

Section 3: BIG DATA Approach

Lecture 6 Fraud Detection - BIG DATA Approach

Lecture 7 Supervised Learning

Lecture 8 Unsupervised Learning

Lecture 9 Fraud Cycle

Lecture 10 Fraud Cycle Continues

Lecture 11 High Level Strategy

Lecture 12 Fraud Analytics Benefits

Section 4: Case Study

Lecture 13 Credit Card Fraud

Lecture 14 Example of Credit Card Fraud

Lecture 15 Example of Credit Card Fraud Continues

Section 5: Conclusion

Lecture 16 Conclusion

Basic knowledge in statistics, mathematics, programming,Basic knowledge of using R and excel,Passion to learn and apply