Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
1	DSBE533	LINEAR PROGRAMMING FOR DATA SCIENCE	3+0+0	3	7,5	13.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	DATA SCIENCE
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Endowing students with concepts, techniques and tools to design, analyze and improve operational capabilities of an organization. Providing an understanding of importance of linear programming for data science in the overall business strategy of the firm. Developing students’ problem solving and critical thinking abilities in improving organization capability.
Course Content	The main purpose of this course is to introduce the theory, algorithm and calculation methods of linear programming. It covers the topics such as modeling linear programming problems, mathematical analysis of linear programming (polyhedral theory, duality, optimality conditions, level of complexity), network flow models, simplex algorithm and its variations, decomposition techniques, interior point methods.
Course Methods and Techniques	Traditional in class lectures and real life applications and cases will be utilized.
Prerequisites and co-requisities	None
Course Coordinator	Associate Prof.Dr. Umut Türk umut.turk@agu.edu.tr
Name of Lecturers	None
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	-
Course Notes	Course materials will be provided throughout the semester

Course Category

Mathematics and Basic Sciences	%40
Social Sciences	%10
Field	%50

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

In-Term Studies	Quantity	Percentage
Yarıyıl İçi Çalışmalarının Başarı Notunun Katkısı	1	% 20
Quiz/Küçük Sınav	3	% 20
Ödev	2	% 20
Final examination	1	% 40
Total	7	% 100

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Yazılı Sınav	1	2	2
Ev Ödevi	3	20	60
Sunum için Hazırlık	1	20	20
Sunum	1	2	2
Proje	1	10	10
Takım/Grup Çalışması	3	25	75
Yüz Yüze Ders	14	3	42
Derse Devam	14	1	14
Final Sınavı	1	2	2
Total Work Load			Number of ECTS Credits 7,5 227

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Identify the need for quantitative managerial decision making tools to improve decision making in a business context.
2	Achieve an understanding of building blocks of quantitative managerial decision making models.
3	Achieve solving, analyzing and interpreting quantitative decision making models using spreadsheets, Lingo, GAMS.
4	Classify the best solution with respect to changes in the parameters of the problem.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Course introduction, introduction to linear programming	Review course syllabus	Syllabus
2	Decision problems and modeling process	Review types of managerial decisions	Lecture notes
3	Linear programming: basic structures	Identify components of a model	Textbook chapter
4	Graphical solution method	Draw 2-variable example	Solution examples
5	Simplex method: basic algorithm	Revise simplex procedure	Excel spreadsheet
6	Duality and shadow prices	Learn about dual variables	Reading material
7	Sensitivity analysis	Prepare examples with parameter changes	Lecture notes
8	Midterm Exam	Review previous content	-
9	Linear programming with LINGO	Install and test sample model	Software instructions
10	Modeling and solving with GAMS	Review GAMS syntax	Application materials
11	Transportation and assignment problems	Study classic problem structures	Lecture notes
12	Multi-objective linear programming	Compare objective functions	Article
13	Student implementations and solution demos	Finalize project for presentation	Presentation slides
14	Final exam preparation and course wrap-up	Final revision	Course summary

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=76794&lang=en