Information Package / Course Catalogue
Data Enveloping Analysis
Course Code: EK351
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

Score coefficients by applying advanced analytical methods is to improve statistical data. And, the worst and the best between units is to provide identification

Course Content

Introduction to linear progrmming and goal programming, basic concepts DEA, Software programming in DEA,Data Envelopment Analysis Methods, CRR, BCR, advanced DEA methods, Mixed Models, Super efficiency model, Optional non-variables model, Catogorical variable methods, Malmquist index

Name of Lecturer(s)
Learning Outcomes
1.The knowledge of the understanding of the basic data structure
2.The knowledge of establishing of algorithms of problem solving
3.The skill of adaptation to do relevant topic after getting basic information
4.Ability to solve the problem of establishing a computer algorithm
5.Students learn how to use Data Envelopment Analysis package programs
Recommended or Required Reading
4.William W. Cooper, Lawrence M. Seiford, Kaoru Tone, 2002, Data Envelopment Analysis A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publisher
Weekly Detailed Course Contents
Week 1 - Theoretical
Linear Programming
Week 2 - Theoretical
Goal Programming
Week 3 - Theoretical
Performance Measurement and Performance Measurement Models, Introduction to Data Envelopment Analysis and Basic Concepts and Packet Programs Used in Data Envelopment Analysis
Week 4 - Theoretical
Chares, Cooper, Rhodes (CCR) model
Week 5 - Theoretical
Input Oriented CCR model
Week 6 - Theoretical
Output Oriented CCR model
Week 7 - Practice
Applications
Week 8 - Practice
Applications
Week 9 - Theoretical
Banker, Charnes and Cooper (BCC) modeling
Week 10 - Theoretical
Input Oriented BCC model
Week 11 - Theoretical
Output Oriented BCC model
Week 12 - Theoretical
Additive Models / multiplicative models
Week 13 - Theoretical
Advanced techniques in data envelopment method, Mixed Models, Super efficiency model, Assurance region model
Week 14 - Theoretical
Advanced techniques in data envelopment method, Uncertain variable model • Categorical variable model • Malmquist index
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory124384
Lecture - Practice25316
Midterm Examination1819
Final Examination115116
TOTAL WORKLOAD (hours)125
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
3
3
3
3
3
3
3
3
3
OÇ-2
4
3
3
4
4
4
2
3
3
OÇ-3
4
2
2
2
5
2
2
5
2
OÇ-4
3
3
3
3
3
3
3
3
3
OÇ-5
3
3
2
2
2
2
5
2
2
Adnan Menderes University - Information Package / Course Catalogue
2026