Submitted by: Submitted by onuracar91
Views: 183
Words: 2226
Pages: 9
Category: People
Date Submitted: 11/25/2013 11:07 AM
areIT433 Data Warehousing and Data Mining
— Course Overview and Introduction —
1
Course Overview
Level of Course: Undergraduate Language of Instruction: English Instructor: Gülay Ünel, gulay.unel@isikun.edu.tr, Office: AMF-220, Extension: 7188
Lectures: Wednesday 10AM – 12PM, Friday 9AM – 10AM
Textbook: J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd Ed., Morgan Kaufman Publishers, March 2006, ISBN 1-55860 901-6.
2
Grading Policy (Tentative)
Quizzes and Participation %10 Project %30 Midterm Exam %30 Final Exam %30
3
Introduction
Motivation: Why data mining?
What is data mining?
Data Mining: On what kind of data? Data mining functionality Classification of data mining systems Top-10 most popular data mining algorithms
Major issues in data mining
Overview of the course
4
Why Data Mining?
The Explosive Growth of Data: from terabytes to petabytes
Data collection and data availability
Automated data collection tools, database systems, Web, computerized society
Major sources of abundant data
Business: Web, e-commerce, transactions, stocks, …
Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube
We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets
5
Data Growth
… (http://www.computerworld.com/s/article/9194283/Data_growth_remains_IT_s_biggest_challenge_Gartner_says)
6
Evolution of Sciences
Before 1600, empirical science 1600-1950s, theoretical science
Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding. Over the last 50 years, most disciplines have grown a third, computational branch (e.g. empirical, theoretical, and computational ecology, or physics, or linguistics.)...