VERİ TABANI YÖNETİM SİSTEMLERİ PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
VBA205	VERİ TABANI YÖNETİM SİSTEMLERİ	3	4	2	5

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. YEŞİM NALKESEN AKIN
Instructor(s) of the Course Unit
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The objective of this course is to help students understand the basic structure and operation of database systems, acquire the knowledge and skills necessary for storing, managing, and querying data, and enable them to effectively use databases in data analytics processes.
Contents of the Course Unit:	Database concepts and data models, database design and modeling, and their use and applications in data analytics processes.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Define a database and explain its purpose of use.
Learn the basics of the SQL language.
Write queries in SQL to retrieve specific information from databases.
Use SQL to join different tables.
Retrieve summary information from databases as needed.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	The relevant unit in the course book	Concepts of data, information, and databases; objectives, components, and applications of database management systems	Explanation, problem-solving, practical application
2	The relevant unit in the course book	Database models; examination of relational and non-relational database approaches	Explanation, problem-solving, practical application
3	The relevant unit in the course book	The relational database model; table, row, column, field, record, key, and integrity constraints	Explanation, problem-solving, practical application
4	The relevant unit in the course book	Database design, entity-relationship model, and the concepts of entity, attribute, and relationship	Explanation, problem-solving, practical application
5	The relevant unit in the course book	Converting entity-relationship diagrams to a relational schema	Explanation, problem-solving, practical application
6	The relevant unit in the course book	Normalization, functional dependency	Explanation, problem-solving, practical application
7	The relevant unit in the course book	Introduction to SQL; creating, modifying, and deleting databases and tables using SQL commands	Explanation, problem-solving, practical application
8	The relevant unit in the course book	SQL data manipulation commands; inserting, updating, and deleting data	Explanation, problem-solving, application
9	The relevant unit in the course book	SQL queries and the use of comparison and logical operators	Explanation, problem-solving, practical application
10	-	MID-TERM EXAM	-
11	The relevant unit in the course book	SQL functions; aggregate functions, grouping	Explanation, problem-solving, practical application
12	The relevant unit in the course book	Querying multiple tables; inner, outer, and cross joins	Explanation, problem-solving, practical application
13	The relevant unit in the course book	Basic use of subqueries, views, indexes, and stored procedures	Explanation, problem-solving, practical application
14	The relevant unit in the course book	Practice	Practical application
15	The relevant unit in the course book	Practice	Practical application
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Çiçek, M. (2010). Database Design and the SQL Query Language. Nirvana Publications.

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

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student gains proficiency in the fundamental components of data science and analytics, and is able to practically apply methods related to statistical analysis, data mining, and machine learning.			2
2	The student is capable of analyzing both structured and unstructured data types and can effectively utilize analytical methods to derive meaningful insights from large datasets.					4
3	The student can utilize programming languages such as Python, R, and SQL in data analysis and modeling processes and is able to effectively manage data processing and automation tasks.						5

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can express analytical findings clearly and effectively by using data visualization and result reporting techniques, contributing meaningfully to decision-making processes.		1
2	The student can analyze complex data-driven problems, develop appropriate solutions, and make creative, data-based decisions through the use of scientific research methods.				3

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can analyze problems encountered in the field of data science and analytics, develop solutions by selecting appropriate data analysis techniques, and critically evaluate statistical, algorithmic, and artificial intelligence-based methods.				3

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can effectively use programming languages such as Python, R, and SQL in data science and analytics applications; they are capable of developing practical solutions using data mining, machine learning, big data processing, data visualization, and modeling tools, and can work with real-world datasets.					4

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student is able to take responsibility in individual or team-based projects related to data science and analytics, independently plan and execute complex data-driven tasks, and play an active role in decision-making processes by developing analytical and creative solutions to encountered problems.			2

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student possesses the competence for continuous self-improvement with an awareness of lifelong learning by following current developments, technologies, and methods in the field of data science and analytics; they can rapidly acquire new knowledge and skills and apply them effectively.			2

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student can communicate their work in data science and analytics clearly and effectively through written, oral, and visual means; they are capable of working efficiently in multidisciplinary teams, engaging in effective communication, and developing collaborative solutions.		1

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	The student has a strong command of the concepts, methods, algorithms, and tools specific to the field of data science and analytics; they can carry out data collection, processing, analysis, and interpretation processes in accordance with ethical principles, and act with a sense of responsibility regarding data privacy and security.					4

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	4	56
Preliminary & Further Study	14	2	28
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	4	1	4
Assignment (Homework)	4	2	8
Project Work	1	5	5
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	5	3	15
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	6	6
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	1	3	3
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	46	0	127