MATHEMATICS I PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
MAT141	MATHEMATICS I	1	3	3	4

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. CEYDA CEVAHİR YILDIZ
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	The goal is to teach students about real functions, trigonometric and exponential functions, the concepts of limits and derivatives, and how to solve problems involving them.
Contents of the Course Unit:	Real Functions, Trigonometric and Exponential Functions, Definitions and Applications of Limits and Derivatives.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Defines functions and their types, and explains their characteristics.
Understands limits and continuity, and performs the corresponding analyses.
The derivative can be used to solve various technical problems.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Preparing the lesson plan	Preparation of course materials Introductions, overview of the course, information on teaching methods, Sets, Concept of numbers (real, rational…), Ordering, Intervals, First- and Second-Degree Inequalities	Method
2	Preparing the lesson plan	Functions, Function Graphs, Polynomial Functions, Rational Functions, Integer-valued Functions, Sign Functions, Composite Functions, Concept of Inverse Functions	Method
3	Preparing the lesson plan	Exponential and Logarithmic Functions, Trigonometric and Inverse Trigonometric Functions	Method
4	Preparing the lesson plan	Limits (Limits of a Function and Limit Rules, the Squeeze Theorem)	Method
5	Preparing the lesson plan	Exact Definition of a Limit, One-Sided Limits, Infinite Limits	Method
6	Preparing the lesson plan	Continuity (Continuity at a Point, Continuous Functions, Intermediate Value Theorem, Types of Discontinuity)	Method
7	Preparing the lesson plan	Algebraic and Geometric Meanings of the Derivative, Rules for Differentiation, Chain Rule	Method
8	Preparing the lesson plan	Derivatives of Trigonometric Functions, Derivatives of Exponential Functions, Derivatives of Logarithmic Functions, Derivatives of Implicit Functions	Method
9	Preparing the lesson plan	General Review	Review
10	-	MID-TERM EXAM	-
11	Preparing the lesson plan	Higher-Order Derivatives, Applications of Derivatives, Important Theorems (Rolle’s Theorem, Mean Value Theorem, First Derivative Test for Local Extrema, Concavity, Second Derivative Test for Concavity, Inflection Points, Second Derivative Test for Local Extrema)	Method
12	Preparing the lesson plan	Plotting Graphs Using Derivatives	Method
13	Preparing the lesson plan	L’Hospital’s Rule	Method
14	Preparing the lesson plan	General Review	Review
15	Preparing the lesson plan	General Review	Review
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
George Thomas Thomas; Maurice D. Weir; Joel R. Hass. Thomas' Calculus
Basic and General Mathematics, H. Hilmi Hacısalihoğlu, Mustafa Balcı, Ankara, 1996.
Advanced Mathematics Problems, A. Karadeniz, Çağlayan Publishing House, Istanbul, 2003.

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description	Examination Method
Mid-Term Exam	1	40		Classical Exam
Homework Assessment	1	10
Final Exam	1	50		Classical Exam
TOTAL	3	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.				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.				3
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.				3

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.				3
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.					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.				3

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.				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.					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	3	42
Preliminary & Further Study	14	2	28
Land Surveying	0	0	0
Group Work	0	0	0
Laboratory	0	0	0
Reading	4	2	8
Assignment (Homework)	4	3	12
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	1	1
Preparation for the Final Exam	2	4	8
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	1	2	2
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	41	0	102