PUBLIC RELATIONS AND PUBLICITY
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 Fall	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 :	Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
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 Gathering and Pre-processing
2. Become familiar with Frequent Item Set Detection
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 prepare the students to become communication professionals by focusing on strategic thinking, professional writing, ethical practice and innovative use of traditional and new media
2)	To be able to create effective public relations plans using fundamental planning components that include situation analysis, public profile, objectives, strategies and tactics.
3)	To be able to apply theoretical concepts related to mass communication, consumer behavior, psychology, persuasion,sociology, marketing, and other related fields to understand how public realtions works.
4)	To be able to have the ability to explain and identify problems associated with the relationships between events and facts in the areas of public relations, persuasive communication, communication management, corporate communications.
5)	To be able to analyze primary and secondary research data in the fields of perception and reputation management and corporate communication practices.
6)	To be able to search, write, and design articles, newsletters, and fliers, brochures, and announcements, in styles and formats appropraite various audiences, mediums and settings.
7)	To be able to apply the underlying theories of communication and the necessities of work safety to different types of public relations processes and campaigns.
8)	To be able to develop creative and persuasive management skills in terms of reputation, employee relations, leadership and similar corporate practices.
9)	To be able to take responsibility in an individual capacity or as a team in generating solutions to given scenarios which can occur in public relations processes.
10)	To be able to understand how an organizational culture works and how employees and leaders create messages as a communication tool.
11)	To be able to critically discuss and interpret theories, concepts, methods, tools and ideas in the field of public relations.
12)	To be able to to use information, communication technologies and computer software with the required level of public relations, marketing communication, persuasive communication, communication management, corporate communications.
13)	To be able to explain and describe business marketing activities, economics, business law and global business practices.
14)	To be able to recognize national and international, social and cultural dimensions of public relations.