Hands On Training

Fraud Detection in R

Name: Fraud Detection in R
Brand: Careervira
Price: 73 USD
Availability: InStock
Rating: 5.0 (3 reviews)

DataCamp

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Course Report

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Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

The Association of Certified Fraud Examiners estimates that fraud costs companies $3.7 trillion each year and accounts for five percent of the average company's annual revenues. In the future, fraud attempts are expected to increase. It is imperative that all industries are able to detect fraud. This course will show you how historical data can help detect fraud. We will use robust statistics and digit analysis to detect suspicious patterns that could indicate fraud. The main issues in developing a supervised tool for detecting fraud are the skewness and imbalance of the data as well as the misclassification costs. These problems can be addressed using techniques that use real and artificial data from a variety fraud applications.

Highlights

Rating & Reviews

Top 30 Percentile

Pedagogy

Top 5 Percentile

Parameters

Pedagogy

Acquire all major R Programming skills in this course for seamless integration into your daily life. Develop a versatile skill set, allowing you to confidently apply what you've learned in various practical scenarios, enhancing your daily experiences and overall proficiency. An exceptional course in R Programming, this stands out for its Self Paced learning approach. Learners have the flexibility to progress at their own speed, tailoring the experience to their individual needs. With a focus on cultivating industry-relevant skills, this course ensures that learners attain a skillset aligned with current industry demands.

Rating & Reviews

This highly acclaimed course is among the top-rated in R Programming, boasting a rating greater than 4 and an overall rating of 5.0. Its exceptional quality sets it apart, making it an excellent choice for individuals seeking top-notch learning experience in R Programming.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Unsupervised Learning in R

Supervised Learning in R: Classification

What You Will Learn

Learn to detect fraud with analytics in R

This course will show how learning fraud patterns from historical data can be used to fight fraud

We present techniques to solve these issues and focus on artificial and real datasets from a wide variety of fraud applications

Course Content

Expand all sections

Module 1: Introduction Motivation

Module 2: Social network analytics

Module 3: Imbalanced class distributions

Module 4: Digit analysis and robust statistics

Course Instructors

Bart Baesens

Professor in Analytics and Data Science at KU Leuven

Bart Baesens is professor in Analytics and Data Science at the Faculty of Economics and Business of KU Leuven, and a lecturer at the University of Southampton (UK). He has done extensive research...

Sebastiaan HÃƒÂ¶ppner

PhD researcher in Data Science at KU Leuven

Sebastiaan HÃƒÂ¶ppner is a PhD researcher at the Section of Statistics and Data Science of the Departement of Mathematics at KU Leuven (Belgium). His research is mainly focused on developing new stat...

Tim Verdonck

Professor at KU Leuven

Tim Verdonck is a professor in Statistics and Data Science at the Department of Mathematics of KU Leuven (Belgium). He is also a visiting professor at the School of Economics, Management and Statisti...

Course Reviews

Average Rating Based on 3 reviews

5.0

100%