Lesson plan / INTRODUCTION TO DATA MINING

Lesson Information

Course Credit 3.0
Course ECTS Credit 5.0
Teaching Language of Instruction İngilizce
Level of Course Bachelor's Degree, TYYÇ: Level 6, EQF-LLL: Level 6, QF-EHEA: First Cycle
Type of Course Faculty Elective
Mode of Delivery Face-to-face
Does the course require compulsory or optional work experience? F
Course Coordinator
Instructor (s)
Course Assistant

Purpose and Content

The aim of the course This course will be an introduction to data mining. Topics will range from statistics to machine learning to database, with a focus on analysis of large data sets. Expect at least one project involving real data, that you will be the first to apply data mining techniques to. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. We will survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.
Course Content Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. A number of successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, and stock market investments. The field of data mining has evolved from the disciplines of statistics and artificial intelligence

Weekly Course Subjects

1Introduction: What is data mining? What makes it a new and unique discipline? Relationship between Data Warehousing, On-line Analytical Processing, and Data Mining.
2Data Warehousing
3Data mining process: Data preparation/cleansing, task identification
4Association Rule mining
5Association rules - different algorithm types
6Classification/Prediction
7Classification - tree-based approaches, Neural Networks, etc
8Clustering - statistical approaches. Clustering - Neural-net and other approaches
9Time Series Mining
10Mining Data Streams
11Multi-Relational Data Mining
12Multi-Relational Data Mining
13Data Mining for Fraud Detection
14Project discussion

Resources

1-Jiawei Han and Micheline Kamber,Data Mining: Concepts and Techniques, The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor.Morgan Kaufmann Publishers, August 2000. 550 pages. ISBN 1-55860-489-8.