INTRODUCTION TO DATA MINING

BİL477

Course Code: BİL477
Semester: -
Theory+Practice: 3 + 0
ECTS: 5

Course Type

Elective Course

Course Level

Bachelor's Degree

Year of Study

Semester

ECTS

Name of Lecturer(s)

Instructor Gökhan Memiş

Learning Outcomes

At the end of this course, the students;

Learn Data Mining in Business.
Learn Data Mining Processes and Knowledge Discovery.
Understand Database Support to Data Mining.
Apply several algorithms of data mining techniques.

Mode of Delivery

Face to face

Prerequisites

Recommended Optional Programme Components

None

Course Description

Data Mining in Business ; Data Mining Processes and Knowledge Discovery: Database Support to Data Mining; Overview of Data Mining Techniques; Cluster Analysis ; Regression Algorithms in Data Mining; Neural Networks in Data Mining; Decision Tree Algorithms; Linear Programming-Based Methods; Business Data Mining Applications; Market-Basket Analysis; Text and Web Mining.

Course Contents

WEEK	TOPICS
1^st Week	Introduction to Data Mining
2^nd Week	Data and Data Types
3^rd Week	Data Preprocessing
4^th Week	Cluster Analysis: Basic Concepts and Algorithms
5^th Week	Regression Algorithms in Data Mining
6^th Week	Time Series Analysis
7^th Week	Classification: Basic Concepts and Techniques (Decision Tree Classifier)
8^th Week	Midterm Exam
9^th Week	Naive Bayes Classifier
10^th Week	Rule Based Classification and kNN
11^th Week	Neural Networks in Data Mining
12^th Week	Association Rules in Knowledge Discovery
13^th Week	Text and Web Mining
14^th Week	Advanced Topics: Ensemble Learning & Big Data Mining
15^th Week	Data Mining Ethics, Privacy, and Social Impacts

Recommended or Required Reading

1. Turban E., Sharda R., Delen D., Decision Support and Business Intellegence Systems, 9/E, ISBN:9780132453233, Pearson, 2011
2. Olson D., Shi Y., Introduction to Business Data Mining, ISBN: 0072959711, McGraw Hill, 2007

Planned Learning Activities and Teaching Methods

Lecture
Problem Solving
Project
Report Preparation
Presentation
Other
Questions/Answers

Assessment Methods and Criteria

	Quantity	Percentage (%)
Mid-term	1	30
Assignment	1	10
Quiz	5	10
Project	1	10
Total(%)		60
Contribution of In-term Studies to Overall Grade(%)		60
Contribution of Final Examination to Overall Grade(%)		40
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam	1	1.5	1.5
Preparation for Quiz
Individual or group work
Preparation for Final exam	1	20	20
Course hours	14	3	42
Preparation for Midterm exam	1	15	15
Laboratory (including preparation)
Final exam	1	1.5	1.5
Homework	4	20	80
Quiz	4	1	4
Total Work Load			164
Total Work Load / 30 (h)			5.46
ECTS Credit of the Course			5

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1	X	X	X	X
K2	X	X		X
K3	X	X
K4	X	X	X	X
K5
K6
K7
K8
K9
K10
K11