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
INE5111	Mathematical Programming and Modelling	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:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :
Course Lecturer(s):	Prof. Dr. SEROL BULKAN Assoc. Prof. YÜCEL BATU SALMAN
Recommended Optional Program Components:	N.A.
Course Objectives:	This course aims to introduce students modeling of linear and integer programs, network flow problems and nonlinear programs; to use the simplex algorithm for solving liner programming problems, branch&bound for solving integer programming problems and some solution algorithms for network flow problems; to understand important modeling techniques and solution algorithms; to get insights about graph theory and its applications; and to identify the types of problems and their solution algorithms.

Learning Outcomes

The students who have succeeded in this course;
I. Formulate large-scale problems as an LP, IP or NLP.
II. Identify the type of problems such as linear, integer and nonlinear problems.
III. Analyze the algorithms such as simplex and branch and bound.
IV. Formulate network flow problems and to solve using specially structured algorithms.

Course Content

This course emphasizes modeling of problems as linear programs, mixed integer linear programs, nonlinear programs and network flow programs. In the second half of the course some basic solution algorithms such as simplex and branch and bound, and some network flow programming algorithms are covered.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Linear programming models I
2)	Linear programming models II
3)	Graphical solution approach and Introduction to Simplex Algorithm
4)	Simplex Algorithm
5)	Integer programming models I
6)	Integer programming models II
7)	Branch and Bound Algorithm
8)	Midterm 1
9)	Nonlinear programming models
10)	Network flow programming models I
11)	Network flow programming models II
12)	Network flow algorithms I
13)	Network flow algorithms II
14)	Midterm II

Sources

Course Notes / Textbooks:	N.A.
References:	Various reference books will be available at the library.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 20
Midterms	2	% 40
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	12	3	36
Study Hours Out of Class	3	25	75
Homework Assignments	4	18	72
Midterms	2	3	6
Final	1	3	3
Total Workload			192

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.