In addition to at least 21 credit units of course work (at least 7 credit course), seminar, and research methods and ethic units, the MSc degree candidate has to prepare and successfully defend a MSc thesis. Expected duration to complete the MSc with Thesis Program is 4 semesters; the maximum duration is 6 semesters. However, students should complete course work including non-credit courses in 4 semesters. Advisor appointment has to be made at the beginning of 2nd semester.
Compulsory Courses
| Code |
Course |
Credit |
ECTS Credit |
| DDS500 |
M.S Thesis
|
0 |
50 |
| DDS501 |
Introduction to Data and Decision Science
|
3 |
8 |
| DDS590 |
Graduate Seminar in DDS
|
0 |
10 |
| STAT573 |
Probability and Statistics for Data Science II
|
3 |
8 |
Research Methods and Ethics
Students can take one of the following courses
- CENG590-Research Methods and Ethics,
- EE595-Research Methods and Ethical Issues in Electrical and Electronics Engineering,
- IAM698-Research Methods and Ethics,
- IE746-Research Methods and Ethocs in Industrial Engineering,
- STAT510-Research Methods and Ethics in Statistics,
It is recommended that students take the course from the department of which their thesis advisor or co-advisors are members.
Elective Courses
Elective courses are grouped under 4 tracks. At least one course must be taken from (a), (b), and, (c) tracks; other 2 courses can taken accorrding to the research interests.
| Tracks |
Courses |
(a) Statistics
|
- IAM557-Statistical Learning and Simulation
- STAT466-Multivariate Analysis
- STAT525-Regression Theory and Methods/STAT618-Mathematical Models and Response Surface Methodology
- STAT554-Computational Statistics/STAT556-Advanced Computing Methods in Statistics
- STAT564-Advanced Statistical Data Analysis
- STAT565-Decision Theory and Bayesian Analysis
|
(b) Data Mining/Machine Learning/Computational Methods
|
- CENG501-Deep Learning
- CENG514-Data Mining/IE460-Data Mining
- CENG562-Machine Learning
- CENG564-Pattern Recognition/EE583-Pattern Recognition
- EE543-Neurocomputers and Deep Learning
|
(c) Optimization
|
- EE553-Optimization
- IAM566-Numerical Optimization
- IAM771-Optimization Methods for Machine Learning
- IE505-Heuristic Search
- IE553-Linear Optimization
- IE558-Multiobjective Decision Making
- OR520-Dynamic Decision Models
|
(d) Free Electives
|
- CENG502-Advanced Deep Learning
- CENG553-Database Management Systems
- CENG556-Distributed Database Management System
- CENG559-Data Security and Protection
- CENG561-Artifıcial Intelligence/EE586-Artificial Intelligence
- CENG568-Knowledge Engineering
- CENG574-Statistical Data Analysis
- CENG576-Numerical Methods in Optimization
- CENG596-Information Retrieval
- CENG740-New Approaches and Applications of Pattern Analysis
- CENG778-Web Search Engine Design
- CENG784-Statistical Methods in Natural Language Processıng
- CENG790-Big Data Analytics
- CENG796-Deep Generative Models
- EE501-Linear System Theory I
- EE531-Probability and Stochastic Processes
- EE535-Communication Theory
- EE5420-Machine Learning by Probabilistic Models
- EE557-Estimation Theory
- IAM508-Computer Algebra
- IAM511-Algorithms and Complexity
- IAM527-Advanced Calculus and Integration
- IAM561-Introduction to Scientific Computing I
- IAM564-Basic Algorithms and Programming
- IAM565-Introduction to Algorithms and Complexity
- IAM715-Cryptogtaphy and Coding Theory
- IE518-Quantitative Methods in Supply Chain Management
- IE554-Discrete Optimization
- IE555-Nonlinear Optimization
- IE560-Stochastic Programming
- IE561-Stochastic Process
- IE571-System Simulation
- OR519-Mathematics for Operations Research
- STAT471-Introduction to Financial Engineering
- STAT497-Applied Time Series Analysis/IAM526-Time Series Applied to Finance
- STAT518-Statistical Analysis of Designed Experiments
- STAT555-Advanced Computational Statistics
- STAT561-Panel Data Analysis
- STAT562-Univariate Time Series Analysis
- STAT563-Multivariate Time Series Analys
- STAT567-Biostatistics and Statistical Genetics/STAT462-Biostatistics
- STAT621-Robust Statistics
- STAT623-Spatial Statistics
- STAT730-Statistics for Bioinformatics
|
See also the following pages for more details on courses:
