USEFUL INFORMATION, RESOURCES & SERVICES FOR STUDENTSUSEFUL LINKS AND DOCUMENTS ADITIONAL & SUPPORTING INFORMATION

SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
DOT6217	Numerical Methods and Applications	3	Fall	3	0	3	6

Course Type :	Elective Course V
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 ÖZGE DEMİR
Dersin Öğretim Eleman(lar)ı:	Instructor ÖZGE DEMİR
Dersin Kategorisi:	Programme Specific

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

Developing problem solving skills using numerical methods and computer programs. It is the creation of the digital infrastructure required for writing.

Course Content:

Basic Python usage, Scratch program usage, number systems, error analysis, basic analysis of algorithms (Flow diagrams, Kruskal, Greed algorithm, Euclidean, Magic Square, etc.), matrix operations, recursive functions, numerical derivative and integral, interpolation, data visualization
and graphing operations with matploit, data analysis with pandas, determining the roots of equations with numerical methods. finding (trapezoidal, beam, Newton-raphson), pigeonhole principle, data types, basic computer measurement numerical analysis of various games (xox, nim, saying zero loses, coin flip, win-win, postman, surviving slave), Nash theorem, graph theorem

Course Specific Rules

There is no specific 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) Defines the algorithm elements of the programming process.
2) Error analysis explains the basic terminology of the field.
3) Explains data structures.
4) Explain the basic computer concepts.
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) To have the ability to develop algorithms
2) To have the ability to write programs in the Python language
3) To have knowledge about data structures and data analysis
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) Applies the numerical methods acquired in code writing projects.

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction of the Python program, Basic computer terms (data types), Computer measurement units, Number systems (2,8,16 number systems-ASCII codes) Analysis of the algorithms of Nurikabe, Sudoku and lights out game problems	None	-
2)	Numerical Error Analysis, Error sources, Usage of small and large numbers in the same process, Types of Errors; Truncation error, Rounding error, Error Calculation Methods, Absolute error, Relative error, Approximation error	None	-
3)	Basic Algorithm analysis- flow diagrams, Kruskal Greed algorithm, Magic square algorithm (for 3x3 and 4x4), Euclidean algorithm, Card sequencing algorithm, Sorting algorithms, Fast sorting, Bubble sorting, Hidden secrets of modern life documentary	None	-
4)	Matrix Operations, Matrix types (identity matrix, symmetric ...), Four operations in matrices, Determinant of matrices, Transposition of matrices, trace, Finding inverse matrix, Covariance matrix, Numerical operations on matrices with Python Numpy, Quiz-1 (5%)	None	-
5)	Recursive functions, Numerical derivative, Numerical integral, Interpolation	None	-
6)	Data analysis with Python Pandas	None	-
7)	Data visualization Quiz-2 with Python Matploit (5%)	None	-
8)	Mid-term (%30)	None	-
9)	Finding the roots of equations by numerical methods, Trapezoidal, Beam Method, Newton-Raphson, Pigeonhole principle and its applications	None	-
10)	Nash Theorem, Prisoner-prisoner dilemma (Game theory), John Nash documentary program	None	-
11)	Min-Max theorem (XOX), Concept of symmetry (round checkers), Postman question (girl's age), Algorithm to find the surviving slave with the king, Racehorse problem, Hanoi towers numerical analysis, Poker return matrix	None	-
12)	The theory of the table game and its application with Python,	None	-
13)	Finding the money in the pocket game, 9 Billiard games, Dividing the clock into three zones game, 4 +4 numbers / 137 / numbers game Finding gold by digital measuring game, Maze game, Competitive-collaborative games,	None	-
14)	Graph theory (graph theorem), Three-door game (Monty Hall problem), X and Y game, Representation of Finite and Infinite games, Birthdays (statistical calculation), Fizz-buzz game with Python, Digital game design with Scratch program, Daisy Horoscope, Game factors, Fraudulent dice problem	None	-

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

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	5	6	7	8
Program Öğrenme Çıktıları (PÖÇ)
1) can define the concepts of computer science and design techniques required in Digital Game Design.
2) can Interpret the historical and theoretical information about analog and digital games.
3) The ability to think in three dimensions and apply this in a digital environment.
4) Avrupa Dil Portfolyosunun en az B1 düzeyinde tanımlanan Yabancı Dilde (İngilizce) iletişim kurma yetkinliği kazanabilme
5) can analyzes the design elements in the game using the theoretical knowledge.
6) can list drawing and animation techniques in the context of discipline.
7) can uses artificial intelligence techniques in the game development process and calculates probabilities based on mathematics and physics rules.
8) Ability to use animation knowledge for digital games designed to be offered on different platforms.
9) can uses freehand drawing and digital drawing techniques.
10) can follows advanced technologies and developments about digital transformation.
11) can have awareness for ethical and social responsivity.
12) can construct the game design and game elements by using them in non-game areas.

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 define the concepts of computer science and design techniques required in Digital Game Design.	2
2)	can Interpret the historical and theoretical information about analog and digital games.	1
3)	The ability to think in three dimensions and apply this in a digital environment.
4)	Avrupa Dil Portfolyosunun en az B1 düzeyinde tanımlanan Yabancı Dilde (İngilizce) iletişim kurma yetkinliği kazanabilme	1
5)	can analyzes the design elements in the game using the theoretical knowledge.	2
6)	can list drawing and animation techniques in the context of discipline.	2
7)	can uses artificial intelligence techniques in the game development process and calculates probabilities based on mathematics and physics rules.	1
8)	Ability to use animation knowledge for digital games designed to be offered on different platforms.	2
9)	can uses freehand drawing and digital drawing techniques.	1
10)	can follows advanced technologies and developments about digital transformation.	1
11)	can have awareness for ethical and social responsivity.	2
12)	can construct the game design and game elements by using them in non-game areas.	1

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
Problem Solving
Demonstration
Views
Reading
Homework
Project Preparation
Course Conference
Brain Storming
Questions Answers
Individual and Group Work

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
Presentation
Final Exam
Quiz
Practice Exam
Participation in Discussions

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

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Quizzes	1	% 10.00
Homework Assignments	1	% 10.00
Midterms	1	% 30.00
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	2	28
Laboratory	0	0	0
Application	2	10	20
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	0	0	0
Presentations / Seminar	0	0	0
Project	0	0	0
Homework Assignments	1	5	5
Total Workload of Teaching & Learning Activities	-	-	53
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	2	10	20
Midterms	1	15	15
Semester Final Exam	1	15	15
Total Workload of Assesment & Evaluation Activities	-	-	50
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			103
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6