DATA MINING PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
YEM406	DATA MINING	8	3	3	8

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	, TYY: + , EQF-LLL: , QF-EHEA:
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	This course aims to teach the students Data Mining Concepts, Data Preparation Techniques, Statistical Learning Theory (Naive Bayes), Clustering Methods (K-Means, hierarchical), Decision Trees and Decision Rules, Association Rules under the new media environment and the acquis.
Contents of the Course Unit:	Contents of the course include the subjects such as finding and discovering useful information in the new media environment in accordance with the purpose of data mining, describing the current situation using the information discovered and predicting future occurrences.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Understand the basic concepts of data mining.
List data mining methods such as clustering, classification, association.
Apply the data mining.
Analyse the data imported from the source.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Literature Review	Introduction to Data Mining	Lecture
2	Literature Review	New Media and Data Mining Relation	Lecture
3	Literature Review	Computer Systems and Data Mining	Lecture
4	Literature Review, Using Visual Sources	Data Mining Applications	Lecture
5	Literature Review	Introduction to Data Mining Algorithms	Lecture
6	Literature Review, Using Visual Sources	Flow-charting	Lecture
7	Literature Review	Basic Concepts in data communications	Lecture
8	-	MID-TERM EXAM	-
9	Literature Review	Introduction to Programming Languages	Lecture
10	Literature Review, Using Visual Sources, Using Applications	Introduction to Database and Management	Lecture
11	Using Visual Sources, Using Applications	Usage of Software Tools in Computer Labs	Demonstrating on Computer Programme
12	Using Visual Sources, Using Applications	Introduction to Programming Languages	Demonstrating on Computer Programme
13	Using Visual Sources, Using Applications	Mathematical process in programming language, controls and cycles	Demonstrating on Computer Programme
14	Using Visual Sources, Using Applications	Base-Calling Algorithms	Lecture & Demonstrating on Computer Programme
15	Using Visual Sources, Using Applications	Processing Algorithms	Lecture & Demonstrating on Computer Programme
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Silahtaroglu G. (2016). Veri Madenciligi Kavram ve Algoritmaları. Istanbul: Papatya Bilim
Karacan H., Yesilbudak M. (2010). Kullanici Merkezli Interaktif Veri Madenciligi: Bir Literatur Taramasi. Ankara: Bilisim Teknolojileri Dergisi
Zaki M. J., Wagner M. J. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge: Cambridge University Press

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	List the history of communication, mass media, communication theories and leading theorists.	0
2	List the historical, social and cultural types of communication and explain the related concepts.		1
3	Define the important points of the history and theories of communication through daily life practices and social life.		1

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Compare the traditional media and new media economic policies.		1
2	Interpret digital culture with constantly updated and self-renewing topics.				3
3	Interpret the technical, socio-political and legal aspects of cyber security issues in the field of new media.				3

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Define the basic concepts of communication history, communication theories, traditional and new media channels.		1

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Prepare web pages with CSS codes.		1
2	Produce creative content in new media environments, create an image and sound and practical studies about programming.		1
3	Analyze the sub-texts and their semantics of the studies presented to the society by mass media.			2
4	Use qualitative and quantitative elements to construct arguments on studies in the field of communication.					4

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Manage social media accounts of brands, corporate firms and public institutions thanks to its advanced knowledge in content production and user experience.		1

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Review local and foreign studies in the field of New Media. Creates innovative works in his/her field.			2
2	Criticize the effects of social media activities on socio-political field.			2

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Plan scientific studies in any area that can be encountered in different disciplines and transfer them to people from different disciplines.				3
2	Determine how much of the content produced by the media is right and wrong.					4

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Follows the developments that have begun to guide the present and the future such as "Software". Produce various software products for different sectors.		1
2	Design using new architectures of big data processing systems.						5
3	Determine the logic of operation of artificial intelligence algorithms and determines the possible effects on media and indirectly society.			2

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	13	6	78
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	0	0	0
Assignment (Homework)	0	0	0
Project Work	0	0	0
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	1	42	42
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	1	40	40
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	31	0	204