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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 : BACHELOR'S DEGREE, TYY: + 6.Level, EQF-LLL: 6.Level, QF-EHEA: First Cycle
Type of the Course : Compulsory
Mode of Delivery of the Course Unit -
Coordinator of the Course Unit Assist.Prof. TAYLAN MARAL
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

CONTRIBUTION OF THE COURSE UNIT TO THE PROGRAMME LEARNING OUTCOMES

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
Total Workload of the Course Unit 204
Workload (h) / 25.5 8
ECTS Credits allocated for the Course Unit 8,0