Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
6MIS 351Simulation and Modelling3+0+03527.07.2026
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program Management Information Systems (English)
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course to introduce some fundamental techniques in M&S and build an understanding of the systems and tools of this field.
Course Content This course introduces students to the concepts and techniques of simulation and
modelling using Python. It covers the fundamentals of Python programming, data
manipulation, and the application of simulation techniques to solve real-world
problems. The course also emphasizes the use of libraries such as NumPy and
Pandas for data analysis and simulation tasks.
Course Methods and Techniques Lecturing, discussion, and submission.
Prerequisites and co-requisities None
Course Coordinator Asist Prof.Dr. IRMAK UZUN BAYAR irmak.bayar@ankarabilim.edu.tr
Name of Lecturers Asist Prof.Dr. IRMAK UZUN BAYAR
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources 50 questions book, ? Course handouts
Course Notes Banks, J. 1998. Handbook of Simulation:Principles, Methodology, Advances,
Application, and Practice, Wiley.
Banks, J. 1998. Handbook of Simulation:Principles, Methodology, Advances,
Application, and Practice, Wiley.
Banks, J., Carson II, J.S., Nelson, B.L., Nicol ,D.M. 2004. Discrete-Event System
Simulation(Fourth Edition), Prentice Hall.
Documents 50 questions book, ? Course handouts


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
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 5 70
Assignments 14 2 28
Mid-terms 1 2 2
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 4,8 144

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 A student who completes SM with success; ? Uses the Google Colaboratory as a coding environment, ? Learns the essentials of functional programming in Python programming language, ? Facilitates the two main data science modules: numpy, and pandas, ? Learns using the Pandas modüle and “boolean masking” method in database management, ? Comprehends the patterns of the central tendancy theorem, ? Learns approximating actual solutions for probability questions.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Concepts of System, Model, and Simulation
2 The purpose of a simulation, its advantages and disadvantages, application areas, types of simulation
3 Monte Carlo simulation, Continuous system simulation, Discrete event simulation, Simulation clock, Time advance mechanisms
4 Monte Carlo simulation, Continuous system simulation, Discrete event simulation, Simulation clock, Time advance mechanisms
5 Random Numbers, Random Number Generators
6 Middle-square method, LCG, Inverse Transform, Convolution, Composition, Acceptance-rejection,
7 Mid- term exam
8 Middle-square method, LCG, Inverse Transform, Convolution, Composition, Acceptance-rejection (Con’t)
9 Generate Random Numbers from some discrete probability distribution
10 Generate Random Numbers from some continuous probability distributions
11 Data collection, Identifying the distribution with data, MLE, Goodness-of-fit tests (Chi-Square Test, Kolmogorov-Smirnov test), Arrival process
12 Input Modeling
13 Basic concepts, Verification techniques, Calibration and validation of models
14 Output Modeling
15 Discussion
16 Final

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 5 5 5 5 5 5 5 5 5 5
C1 5 5 5 5 5 5 5 5 5 5

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