PYTHON PROGRAMLAMA PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
VBA207	PYTHON PROGRAMLAMA	3	4	2	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
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The purpose of this course is to introduce students to the fundamental concepts, data structures, and core libraries of the Python programming language, thereby equipping them with programming skills they can apply in data analysis processes. The course aims to provide students with hands-on experience in basic data processing and data analysis applications.
Contents of the Course Unit:	Introduction to Python programming, variables, data types, operators, conditional statements, loops, functions, standard libraries, data processing, and basic data analysis applications.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Use fundamental programming concepts in Python, such as variables, data types, operators, loops, conditional statements, and functions.
Develop algorithms for real-world problems and implement them using the Python programming language.
Debug Python code.
Perform basic data analysis and visualization on datasets using the Python programming language.
Integrate ready-made solutions using modules and libraries from the Python ecosystem and collaborate on projects.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Review	Introduction, Python Development Environments, and Basic Syntax	Explanation
2	Review of sample code developed in the first week	Variables, Data Types, Operators, and Basic Input/Output Operations	Explanation, problem-solving, practical application
3	Examination of examples involving comparison and logical operators	Control Structures: if, elif, and else Blocks	Explanation, problem-solving, practical application
4	Review of examples related to decision structures	Loops: for, while, nested loops, break, and continue	Explanation, problem-solving, practical application
5	Review of decision structure and loop examples	Decision Structures and Loop Applications	Explanation, problem-solving, practical application
6	Review of loop examples	Strings, Lists, and Tuples	Explanation, problem-solving, practical application
7	Review of list and tuple examples	Dictionaries and Sets	Explanation, problem-solving, practical application
8	Review of dictionary and set examples	Functions: Parameters, Default Values, return, and Variable Scope	Explanation, problem-solving, practical application
9	Review of function examples	Modular Programming, the Python Standard Library, Package Management	Explanation, problem-solving, practical application
10	-	MID-TERM EXAM	-
11	Examination of Sample Text and Data Files	File Operations, Error Handling, and Basic Data Analysis	Explanation, problem-solving, practical application
12	Examination of a Sample Dataset	Basic Data Analysis and Visualization Applications	Explanation, problem-solving, practical application
13	Review of code examples	Basic Interactive Application Development and Integrated Applications	Explanation, problem-solving, practical application
14	Review of code examples	Fundamentals of Interactive Application Development and Integrated Applications	Explanation, problem-solving, practical application
15	Review of code examples	Fundamentals of Interactive Application Development and Integrated Applications	Explanation, problem-solving, practical application
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Özgül, F. (2017). Python in All Its Aspects (5th ed.). Kodlab 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.				3
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.			2
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.			2
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.					4

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.						5

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.				3

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.				3

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.			2

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.				3

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	3	42
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	8	2	16
Assignment (Homework)	1	4	4
Project Work	3	4	12
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	8	3	24
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	1	2	2
Final Exam	1	1	1
Preparation for the Final Exam	2	4	8
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	2	4	8
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	55	0	174