Course Detail

Course Description Course Outline Course Contribution to Program Qualifications Assessment Methods ECTS-Workload Detail Informations of the Course Numerical Data

Course Description

Course	Code	Semester	T+P (Hour)	Credit	ECTS
INTRODUCION to PROGRAMMING	İNM1117449	Fall Semester	3+2	4	6

Course Program

Cuma 18:30-19:15

Cuma 19:30-20:15

Cuma 20:30-21:15

Prerequisites Courses
Recommended Elective Courses

Language of Course	Turkish
Course Level	First Cycle (Bachelor's Degree)
Course Type	Elective
Course Coordinator	Prof.Dr. Selim AKYOKUŞ
Name of Lecturer(s)	Prof.Dr. Selim AKYOKUŞ, Malik GEYLANİ
Assistant(s)
Aim	This course introduces fundamentals of programming, problem solving and algorithm development for students with little or no prior programming experience using Pyhton programming language. The objective of this course is to prepare students for more advanced programming courses as well as providing an understanding of computation in problem solving and engineering as a self-contained course for those students who want to write programs for their studies and professional work. The course emphasizes structured programming, algorithmic and object thinking in a problem-driven way after teaching fundamental concepts and structures. Topics include an introduction to computers, programming languages and Pyhton; elementary programming, selections, data types, strings, iteration, functions, GUIs (graphical user interfaces), objects and classes, inheritance and polymorphism, lists (arrays) and multidimensional lists, sets and dictionaries, files, exceptions and recursion. Weekly laboratories and assignments with different problems, practice and coding exercises will improve student's capabilities and fluency in programming.
Course Content	This course contains; Introduction to Computers, Programming, and Python,Elementary Programming,Mathematical Functions, Strings, and Objects,Selection statements,Loops,Loops,Functions,Lists, Multidimensional Lists, Tuples, Sets, and Dictionaries,Basic GUI Programming,Advanced GUI Programming,Inheritance and Polymorphism,Files and Exception Handling,Recursion,Developing Efficient Algorithms.

Course Learning Outcomes	Teaching Methods	Assessment Methods
Analyze and design strategies for solving basic programming problems.	12, 14, 16, 17, 9	A, E, F
Learn how to use files, exceptions and build GUIs.	12, 14, 16, 17, 2, 9	A, E, F
Develop custom classes using encapsulation, polymorphism, inheritance, and abstraction.	12, 14, 16, 17, 2, 9	A, E, F
Understand the differences between procedural and object-oriented paradigms.	12, 14, 17, 9	A, E, F
Use control statements, loops, functions, and lists.	12, 14, 17, 9	A, E, F
Understand programming concepts and techniques using Python Language.	12, 16, 9	A, E

Teaching Methods:	12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 17: Experimental Technique, 2: Project Based Learning Model, 9: Lecture Method
Assessment Methods:	A: Traditional Written Exam, E: Homework, F: Project Task

Course Outline

Order	Subjects	Preliminary Work
1	Introduction to Computers, Programming, and Python
2	Elementary Programming
3	Mathematical Functions, Strings, and Objects
4	Selection statements
5	Loops
6	Loops
7	Functions
8	Lists, Multidimensional Lists, Tuples, Sets, and Dictionaries
9	Basic GUI Programming
10	Advanced GUI Programming
11	Inheritance and Polymorphism
12	Files and Exception Handling
13	Recursion
14	Developing Efficient Algorithms

Resources

- Y. Daniel Liang, Introduction to Programming Using Python, 2nd Ed., Pearson, 2019. - Allen B. Downey, Think Python How to Think Like a Computer Scientist 2nd Ed., OReilly Media, 2015.

Lecture notes that will be delivered during the classes.

Course Contribution to Program Qualifications

Course Contribution to Program Qualifications
No	Program Qualification	Contribution Level
No	Program Qualification	1	2	3	4	5
1	An ability to apply knowledge of mathematics, science, and engineering.
2	An ability to identify, formulate, and solve engineering problems.
3	An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability.
4	An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
5	An ability to design and conduct experiments, as well as to analyze and interpret data.
6	An ability to function on multidisciplinary teams.
7	An ability to communicate effectively.
8	A recognition of the need for, and an ability to engage in life-long learning.
9	An understanding of professional and ethical responsibility.
10	A knowledge of contemporary issues.
11	The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context.

Assessment Methods

Contribution Level	Absolute Evaluation
Rate of Midterm Exam to Success		30
Rate of Final Exam to Success		70
Total		100

ECTS / Workload Table
Activities	Number of	Duration(Hour)	Total Workload(Hour)
Course Hours	42	1	42
Guided Problem Solving	10	7	70
Resolution of Homework Problems and Submission as a Report	8	3	24
Term Project	0	0	0
Presentation of Project / Seminar	0	0	0
Quiz	2	5	10
Midterm Exam	1	22	22
General Exam	1	22	22
Performance Task, Maintenance Plan	0	0	0
Total Workload(Hour)			190
*Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30=(190/30)**			6
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.

Detail Informations of the Course