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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 :	Instructor 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	Workload
Course Hours	14	42
Study Hours Out of Class	16	32
Project	5	15
Homework Assignments	6	12
Midterms	8	28
Final	6	26
Total Workload		155

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 be able to apply theoretical concepts related to mass communication, consumer behavior, psychology, persuasion,sociology, marketing, and other related fields to understand how advertising and brand communication works in a free-market economy.	2
2)	To be able to critically discuss and interpret theories, concepts, methods, tools and ideas in the field of advertising.	2
3)	To be able to research, create, design, write, and present an advertising campaign and brand strategies of their own creation and compete for an account as they would at an advertising agency.	2
4)	To be able to analyze primary and secondary research data for a variety of products and services.	2
5)	To be able to develop an understanding of the history of advertising as it relates to the emergence of mass media outlets and the importance of advertising in the marketplace.	2
6)	To be able to follow developments, techniques, methods, as well as research in advertising field; and to be able to communicate with international colleagues in a foreign language. (“European Language Portfolio Global Scale”, Level B1)	2
7)	To be able to take responsibility in an individual capacity or as a team in generating solutions to unexpected problems that arise during implementation process in the Advertising field.	3
8)	To be able to understand how advertising works in a global economy, taking into account cultural, societal, political, and economic differences that exist across countries and cultures.	2
9)	To be able to approach the dynamics of the field with an integrated perspective, with creative and critical thinking, develop original and creative strategies.	2
10)	To be able to to create strategic advertisements for print, broadcast, online and other media, as well as how to integrate a campaign idea across several media categories in a culturally diverse marketplace.	2
11)	To be able to use computer software required by the discipline and to possess advanced-level computing and IT skills. (“European Computer Driving Licence”, Advanced Level)	2
12)	To be able to identify and meet the demands of learning requirements.	2
13)	To be able to develop an understanding and appreciation of the core ethical principles of the advertising profession.	2