FE581 – 1005 – Introduction to Data Mining

Introduction

Data that are needed for making managerial decisions is accumulating at an increasing rate due to a number of technological advances. As a result of innovations such as the internet, electronic banking, point-of-sale devices, barcode readers and e-tailers, electronic data collection has turned out to be inexpensive. Consequently, data warehouses and data marts designed for managerial decision support contain huge amounts of data. Data mining that evolved from the disciplines of statistics and artificial intelligence is concerned with applying various techniques to make intelligent use of data repositories. There have been several successful applications in areas such as credit rating, database marketing, fraud detection, stock market investments and customer relationship management.This course will examine methods that proved to be useful in recognizing patterns and making predictions. We will review applications and provide an opportunity for hands-on experimentation with data mining algorithms. At the end of the course students will have developed an understanding of the strengths and limitations of popular data mining techniques.

Textbook

An Introduction to Statistical Learning with Applications in R, Second edition, written by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer.

It can be downloaded on the website https://www.statlearning.com/

Introduction Slide

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File: Introduction