VERİ BİLİMİNE GİRİŞ PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
VBA105	VERİ BİLİMİNE GİRİŞ	1	3	3	7

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	Assist.Prof. BANU KAYINOVA
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The course aims to introduce students to the fundamental concepts of data science, help them develop data-driven thinking skills, and provide an introduction to the processes of data collection, processing, analysis, and interpretation.
Contents of the Course Unit:	The concept of data science, types of data, data collection methods, basic statistical concepts, data cleaning and preprocessing, data visualization techniques, algorithmic thinking and the logic of data analysis, application areas of data science, ethics and privacy issues, big data, and future trends in data science.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Explains the concepts of data science and data analytics.
Defines data sources and data types.
Generates simple statistical summaries.
Understands data cleaning and organization methods.
Interprets data visualization techniques.
Explains the concept of algorithmic thinking and discusses its role in the data analysis process.
Explains the applications of data science in various disciplines, using examples.
Discusses data ethics and privacy principles.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	The relevant unit in the course book	Introduction.	Explanation, problem-solving, practical application
2	The relevant unit in the course book	Data types and data sources.	Explanation, problem-solving, practical application
3	The relevant unit in the course book	Data collection methods and basic data structures.	Explanation, problem-solving, practical application
4	The relevant unit in the course book	Data cleaning and preprocessing (missing/erroneous data).	Explanation, problem-solving, practical application
5	The relevant unit in the course book	Basic statistical concepts (mean, median, variance).	Explanation, problem-solving, practical application
6	The relevant unit in the course book	Introduction to data visualization (tables, graphs).	Explanation, problem-solving, practical application
7	The relevant unit in the course book	Algorithmic thinking and the logic of data analysis (language-independent).	Explanation, problem-solving, practical application
8	The relevant unit in the course book	Examples of simple analysis on datasets.	Explanation, problem-solving, practical application
9	The relevant unit in the course book	Introduction to data mining and example applications.	Explanation, problem-solving, practical application
10	-	MID-TERM EXAM	-
11	The relevant unit in the course book	Introduction to machine learning (basic concepts).	Explanation, problem-solving, practical application
12	The relevant unit in the course book	Application areas of data science (healthcare, finance, education, etc.).	Explanation, problem-solving, practical application
13	The relevant unit in the course book	Big data and cloud-based data processing.	Explanation, problem-solving, practical application
14	The relevant unit in the course book	Ethical principles in data science and future trends.	Explanation, problem-solving, practical application
15	The relevant unit in the course book	General review	Explanation, problem-solving, practical application
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Çelik, A. (2021). Introduction to Data Science. Ankara: Nobel Academic Publishing.
Kılınç, D. & Başeğmez, N. (2019). Data Science with Applications: Artificial Intelligence and Machine Learning.
Lecture notes

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	50		Classical Exam
Final Exam	1	50		Classical Exam
TOTAL	2	100

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.						5
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.						5
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.					4

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.						5
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.						5

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.						5

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.						5

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.						5

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.						5

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.						5

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	14	3	42
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	14	3	42
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	3	3
Preparation for the Final Exam	1	25	25
Mid-Term Exam	1	3	3
Preparation for the Mid-Term Exam	1	25	25
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	46	0	182