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Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP4336	Introduction to Data Mining	Spring	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. BARIŞ ÖZCAN
Recommended Optional Program Components:	None
Course Objectives:	In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis.

Learning Outcomes

The students who have succeeded in this course;
1. Be able to understand Data Pre-processing and meaningful statistics
2. Become familiar to Machine Learning
3. Be able to understand Association Rule Mining
4. Be able to understand Classifiers, and their benefits
5. Be able to use Clustering
6. Be able to understand Clustering Evaluation

Course Content

1.Introduction to Basic Concepts
2.Data Exploration
3.Classification
4.Clustering
5.Dimensionality Reduction
6.Frequent Item Set Mining
7.Association Rule Mining

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Basic Concepts	None
2)	Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis	None
3)	Data Pre-Processing, Transformation, Normalization, Standardization	None
4)	Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation	None
5)	Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers
6)	Classification	None
6)	Classification and Regression: Artificial Neural Networks, Support Vector Machines
7)	Midterm I	Review of all topics covered so far
8)	Clustering: Partitioning and Hierarchical Algorithms	None
9)	Clustering: Density-Based Algorithms
10)	Cluster Evaluation, Comparing Clusterings	None
11)	Midterm II	none
12)	Dimensionality Reduction	none
13)	Frequent Item Set Mining	none
14)	Association Rule Mining	none

Sources

Course Notes / Textbooks:	Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar
References:	Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	2	% 20
Project	1	% 20
Midterms	2	% 20
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Total Workload

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To prepare students to become communication professionals by focusing on strategic thinking, professional writing, ethical practices, and the innovative use of both traditional and new media	2
2)	To be able to explain and define problems related to the relationship between facts and phenomena in areas such as Advertising, Persuasive Communication, and Brand Management
3)	To critically discuss and interpret theories, concepts, methods, tools, and ideas in the field of advertising
4)	To be able to follow and interpret innovations in the field of advertising
5)	To demonstrate a scientific perspective in line with the topics they are curious about in the field.
6)	To address and solve the needs and problems of the field through the developed scientific perspective
7)	To recognize and understand all the dynamics within the field of advertising
8)	To analyze and develop solutions to problems encountered in the practical field of advertising