BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
MAT5101	Engineering Mathematics	Fall Spring	3	0	3	8

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	Turkish
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Prof. Dr. MESUT EROL SEZER
Course Lecturer(s):	Assist. Prof. CAVİT FATİH KÜÇÜKTEZCAN
Recommended Optional Program Components:	None
Course Objectives:	To equip the student with advanced topics of vector calculus and complex calculus.

Learning Outcomes

The students who have succeeded in this course;
The student will be able to understand differences and similarities of fundamental mathematical concepts as they apply to functions of a single variable or several variables, and to apply concepts of advanced calculus and complex calculus to engineering problems

Course Content

Vector differential and integral calculus, and applications. Complex calculus and applications. Fourier series and Fourier transform.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Review of single-variable calculus.
2)	Functions of several variables. Partial derivatives, differentials, implicit functions, Jacobian.
3)	Vector functions. Gradient, divergence, curl and Laplacian. Directional derivative.
4)	Maxima and minima, Lagrange multipliers.
5)	Multiple integrals. Line integrals, Green's theorem.
6)	Surface integrals, the divergence theorem, Stoke's theorem.
7)	Cylindrical and spherical coordinates.
8)	Applications of vector calculus.
9)	Functions of a complex variable. Continuity and differentiation.
10)	Complex integration. Cauchy's theorem and integral formula.
11)	Taylor and Laurent series. Poles and residues.
12)	Conformal mapping and applications.
13)	Fourier series.
14)	Fourier transform.

Sources

Course Notes / Textbooks:
References:	1. D. Bachman, Advanced Calculus Demystified, McGraw-Hill, 2007. 2. F. J. Flanigan, Complex Variables, Dover, 1983.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	15	% 15
Homework Assignments	5	% 15
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	7	98
Homework Assignments	5	5	25
Midterms	1	10	10
Final	1	15	15
Total Workload			190

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.