At the end of this course, the students; 1) Will be able to apply some linear algebra and statistics techniques using MATLAB. 2) Will have a better understanding of data, information and knowledge. 3) Will learn data mining (DM), and data exploration. 4) Will learn Classification, Association Analysis, Cluster Analysis, and Anomaly Detection techniques and apply some algorithms on the given data.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
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
1st Week
Introduction to Data Mining.
2nd Week
DM, Some Linear Algebra and Statistics Techniques and MATLAB applications.
3rd Week
Data-Information-Knowledge : Relations and Differences.
4th Week
Data Mining (DM) and Data Exploration.
5th Week
Classification - General & Algorithms.
6th Week
Classification - Applications.
7th Week
Association Analysis - General.
8th Week
Mid-term
9th Week
Association Analysis - Algorithms and Applications.
10th Week
Cluster Analysis - General.
11th Week
Cluster Analysis - Algorithms.
12th Week
Cluster Analysis - Applications.
13th Week
Anomaly Detection - General.
14th Week
Anomaly Detection - Algorithms and Applications.
RECOMENDED 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
Presentation
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
30
Assignment
1
5
Quiz
1
15
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 WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
2
2
Preparation for Quiz
Individual or group work
Preparation for Final exam
1
30
30
Course hours
14
3
42
Preparation for Midterm exam
Laboratory (including preparation)
Final exam
1
2
2
Homework
2
90
180
Project
1
100
100
Total Workload
356
Total Workload / 30
11,86
ECTS Credits of the Course
10
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
