SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
YTN6011	Büyük Veri Okuryazarlığı	3	Spring	3	0	3	6

Course Type :	University Elective
Cycle:	Bachelor      TQF-HE:6. Master`s Degree      QF-EHEA:First Cycle      EQF-LLL:6. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Instructor FATMA NUR BUDAK
Dersin Öğretim Eleman(lar)ı:	Instructor FATMA NUR BUDAK
Dersin Kategorisi:	Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

The aim of this course is to enable students to recognize big data applications they may encounter in their professional fields; to critically examine the role of data in decision-making, planning, and evaluation processes; and to develop awareness of using data in a conscious, ethical, and critical manner.

Course Content:

Within the scope of this course, the concept of big data and the fundamental principles of data literacy are introduced. The use of big data in everyday life and across different professional fields is examined. Data generation sources, data types, and data quality concepts are presented. Data-driven decision-making processes, basic visualization approaches, and AI-supported applications are discussed at a conceptual level. The course also addresses the ethical, privacy, and legal dimensions of big data, as well as issues of data bias, manipulation, and critical data reading. Students are encouraged to evaluate big data applications within the context of their own disciplines.

Course Specific Rules

Regular attendance is mandatory for this course. A minimum attendance rate of 70% is required; students who fail to meet this requirement will not be eligible to take the final examination. Students are expected to participate actively, contribute to in-class discussions, and submit assignments on time. The use of AI tools within the scope of the course will be evaluated within the framework of ethical and responsible use, and any usage contrary to academic integrity will be considered invalid. In cases of cheating, plagiarism, or similar academic violations, the relevant university regulations will be applied. Unauthorized sharing of course materials is prohibited. Students are expected to follow course announcements regularly and comply with all procedural rules.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

1) Explains the ways big data is used in daily life and in different professional fields.

2) Evaluates data-driven decision-making, planning, and evaluation processes through examples.

3) Discusses the ethical, privacy, and legal dimensions of big data and artificial intelligence applications.

4) Evaluates, at a conceptual level, big data sources, data generation processes, and storage approaches.

5) Recognizes the risks of data bias, manipulation, and misinformation.

Skills (Describe as Cognitive and/or Practical Skills.)

1) Interprets basic data visualizations and critically reads information presented with data.

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

1) Understands the fundamental lifecycle of big data projects and proposes solution approaches for potential bottlenecks in the data-driven value creation process.

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction to data literacy: thinking in terms of data.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
2)	What is big data? The 5V framework.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
3)	Sources of big data and the process of data generation.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
4)	Data collection and data storage.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
5)	Data quality and sources of bias.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
6)	Data visualization and data interpretation.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
7)	Big data and algorithmic systems in practice.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
8)	Midterm
9)	Bias and error in big data.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
10)	Data privacy and ethical principles.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
11)	Sectoral uses of big data.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
12)	Interaction with big data tools.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
13)	Critical literacy in big data.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.
14)	Professional big data literacy and reflection.	Gürsakal, N. (2023). Big data. Nobel Academic Publishing.

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:
References:	Gürsakal, N. (2023). Büyük veri. Nobel Akademik Yayıncılık. Demirkol, Z. (t.y.). Veri bilimi okuryazarlığı e-kılavuzu. SAS Türkiye. Özdoğan, O. (2015). Büyük veri denizi: Veri yönetimi hakkında her şey. Elma Yayınevi. Sağıroğlu, Ş., & Koç, O. (Ed.). (2017). Büyük veri ve açık veri analitiği: Yöntemler ve uygulamalar

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	3	4	6	5	7
Program Öğrenme Çıktıları (PÖÇ)
1) Can explain the fundamental concepts, theories, and models of public relations and advertising.
2) Can define ethical rules, legal regulations, and professional standards in the field of public relations, communication and advertising.
3) Can analyze the social, cultural, economic, and political contexts of public relations, media and advertising.
4) Can develop public relations and advertising campaigns through target audience analysis.
5) Can create innovative communication solutions using traditional and digital media tools.
6) Can evaluate public relations and advertising strategies by conducting effectiveness analysis.
7) Can take responsibility in public relations and advertising projects both individually and in team settings.
8) Can utilize leadership and decision-making skills when determining public relations and advertising strategies.
9) Can follow new trends and technological developments in public relations and advertising.
10) Can generate knowledge in the field of public relations and advertising by using research and analytical skills.
11) Can act in accordance with ethical and social responsibility principles in public relations and advertising.
12) Can plan and implement crisis management, reputation management, and brand management processes.
13) Can establish effective verbal and written communication in public relations and advertising processes.
14) Can develop professional relationships in multicultural and global communication contexts.
15) Can develop digital strategies in public relations and advertising using new media tools.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

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

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Can explain the fundamental concepts, theories, and models of public relations and advertising.
2)	Can define ethical rules, legal regulations, and professional standards in the field of public relations, communication and advertising.
3)	Can analyze the social, cultural, economic, and political contexts of public relations, media and advertising.
4)	Can develop public relations and advertising campaigns through target audience analysis.
5)	Can create innovative communication solutions using traditional and digital media tools.	2
6)	Can evaluate public relations and advertising strategies by conducting effectiveness analysis.
7)	Can take responsibility in public relations and advertising projects both individually and in team settings.
8)	Can utilize leadership and decision-making skills when determining public relations and advertising strategies.
9)	Can follow new trends and technological developments in public relations and advertising.	3
10)	Can generate knowledge in the field of public relations and advertising by using research and analytical skills.	2
11)	Can act in accordance with ethical and social responsibility principles in public relations and advertising.
12)	Can plan and implement crisis management, reputation management, and brand management processes.
13)	Can establish effective verbal and written communication in public relations and advertising processes.
14)	Can develop professional relationships in multicultural and global communication contexts.
15)	Can develop digital strategies in public relations and advertising using new media tools.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Reading
Homework
Project Preparation
Brain Storming
Questions Answers
Individual and Group Work
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Homework Evaluation

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Homework Assignments	1	% 15.00
Midterms	1	% 35.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	3	42
Laboratory	0	0	0
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	14	1.5	21
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	1	20	20
Total Workload of Teaching & Learning Activities	-	-	83
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	1	30	30
Semester Final Exam	1	40	40
Total Workload of Assesment & Evaluation Activities	-	-	70
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			153
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6