DATA MINING

BİS507

Course Code: BİS507
Semester: -
Theory+Practice: 2 + 2
ECTS: 10

Course Type

Elective Course

Course Level

Master's Degree Without Thesis

Year of Study

Semester

ECTS

Name of Lecturer(s)

Professor Mustafa Sert

Learning Outcomes

At the end of this course, the students;

Will be able to apply some linear algebra and statistics techniques using MATLAB.
Will have a better understanding of data, information and knowledge.
Will learn data mining (DM), and data exploration.
Will learn Classification, Association Analysis, Cluster Analysis, and Anomaly Detection techniques and apply some algorithms on the given data.

Mode of Delivery

E-Learning

Prerequisites

Recommended Optional Programme Components

None

Course Description

Introduction to Data Mining DM, Some Linear Algebra and Statistics Techniques and MATLAB applications. Data-Information-Knowledge : Relations and Differences. Data Mining (DM) and Data Exploration. Classification - General. Classification - Algorithms. Classification - Applications. Association Analysis - General. Association Analysis - Algorithms and Applications. Cluster Analysis - General. Cluster Analysis - Algorithms. Cluster Analysis - Applications. Anomaly Detection - General. Anomaly Detection - Application.

Course Contents

WEEK	TOPICS
1^st Week	Introduction to Data Mining.
2^nd Week	DM, Some Linear Algebra and Statistics Techniques and MATLAB applications.
3^rd Week	Data-Information-Knowledge : Relations and Differences.
4^th Week	Data Mining (DM) and Data Exploration.
5^th Week	Classification - General & Algorithms.
6^th Week	Classification - Applications.
7^th Week	Association Analysis - General.
8^th Week	Association Analysis - Algorithms and Applications.
9^th Week	Cluster Analysis - General.
10^th Week	Cluster Analysis - Algorithms.
11^th Week	Cluster Analysis - Applications.
12^th Week	Anomaly Detection - General.
13^th Week	Anomaly Detection - Algorithms and Applications.
14^th Week	Final Exam

Recommended or Required Reading

Course Book: Tan, P.N., M. Steinbach, V. Kumar, "Data Mining", Pearson, 2007.
Additional Resources:
1. Turban, E., Sharda, R. & Delen, D.,"Decision Support and Business Intelligence Systems", Pearson, 9th Ed., 2011.
2. Olson, D. &Shi, Y.,"Introduction to Business Data Mining", McGraw-Hill, 2007.
3. Han, J. and M. Kamber, "Data Mining", Morgan Kaufman, 2.ed. 2006.
4. Mitchell, T.M.,"Machine Learning", McGraw-Hill, 1997.

Planned Learning Activities and Teaching Methods

Lecture

Assessment Methods and Criteria

	Quantity	Percentage (%)
Assignment	1	20
Total(%)		20
Contribution of In-term Studies to Overall Grade(%)		20
Contribution of Final Examination to Overall Grade(%)		80
Total(%)		100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Midterm exam
Preparation for Quiz
Individual or group work	14	3	42
Preparation for Final exam	1	40	40
Course hours	14	3	42
Preparation for Midterm exam
Laboratory (including preparation)
Final exam	1	2	2
Homework
Presentation (including preperation)	1	80	80
Project	1	80	80
Total Work Load			286
Total Work Load / 30 (h)			9.53
ECTS Credit of the Course			10

Language of Instruction

Turkish

Work Placement(s)

Programme Qualifications / Learning Outcomes

	LO1	LO2	LO3	LO4
K1	X			X
K2	X	X	X	X
K3	X	X		X
K4	X	X	X	X
K5	X		X	X
K6	X		X	X
K7				X
K8	X	X	X	X
K9
K10				X
K11
K12

	LO1	LO2	LO3	LO4
K1	X			X
K2	X	X	X	X
K3	X	X		X
K4	X	X	X	X
K5	X		X	X
K6	X		X	X
K7				X
K8	X	X	X	X
K9
K10				X
K11
K12

	LO1	LO2	LO3	LO4
K1	X			X
K2	X	X	X	X
K3	X	X		X
K4	X	X	X	X
K5	X		X	X
K6	X		X	X
K7				X
K8	X	X	X	X
K9
K10				X
K11
K12

	LO1	LO2	LO3	LO4
K1	X			X
K2	X	X	X	X
K3	X	X		X
K4	X	X	X	X
K5	X		X	X
K6	X		X	X
K7				X
K8	X	X	X	X
K9
K10				X
K11
K12