ECTS - Statistical Applications in Industrial Engineering

Statistical Applications in Industrial Engineering (IE442) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Statistical Applications in Industrial Engineering	IE442	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Natural & Applied Sciences Master's Degree
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Demonstration, Experiment, Problem Solving.
Course Coordinator
Course Lecturer(s)	Asst. Prof. Dr. Tuğçe Yavuz
Course Assistants
Course Objectives	The course aims to prepare the student to analyze and classify data and develop empirical models for industrial engineering problems under service/production contexts. The student will be able to distinguish between different statistical techniques and implement them using a statistical software package.
Course Learning Outcomes	The students who succeeded in this course; Students will improve their problem solving skills and their analytical thinking ability. Students will become familiar with a suitable statistical package through computer-based statistical analysis. Students will learn how to collect and analyze data and use statistics to enhance their project objectives. Students will learn to differentiate the common uses and misuses of statistics in business and industrial applications. Students will be able to define and differentiate industrial and systems engineering problems that can be solved using statistical techniques.
Course Content	Applications of simple and multiple linear regression, design and analysis of experiments, multivariate analysis and nonparametric tests for the solution of industrial engineering problems.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Review of Basic Statistical Concepts	Montgomery, Chapter 1
2	Simple Linear Regression	Montgomery, Chapter 2
3	Multiple Linear Regression	Montgomery, Chapter 2
4	Single-Factor Experimental Design and Analysis	Montgomery, Chapter 3
5	Multi-Factor Experimental Design and Analysis	Montgomery, Chapter 3
6	Nonparametric Tests	Montgomery, Chapter 3.11
7	Fundamental Machine Learning Algorithms	James, Chapters 3, 4, 8, 9
8	Fundamental Machine Learning Algorithms	James, Chapters 3, 4, 8, 9
9	Midterm Exam
10	Mathematical Modeling with Gurobi	Winston, Chapters 3, 4
11	Metaheuristics: Single-solution-based methods	Talbi, Chapter 2
12	Metaheuristics: Single-solution-based methods	Talbi, Chapter 2
13	Metaheuristics: Population-based methods	Talbi, Chapter 3
14	Metaheuristics: Population-based methods	Talbi, Chapter 3
15	Neural Networks	James, Chapter 10.1
16	Final Exam

Sources

Course Book	1. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2023). An introduction to statistical learning: with applications in Python, New York: springer.
	2. W. L. Winston, Operations Research: Applications and Algorithm., 4th Edition, Thomson Learning, Inc. 2004.
	3. Talbi, E. G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons.
Other Sources	4. Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.".
	5. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.".
	6. Chong, E. K., Lu, W. S., & Zak, S. H. (2023). An introduction to optimization: with applications to machine learning. John Wiley & Sons.
	7. Sarkar, R. (2023). A Handbook of Mathematical Models with Python: Elevate your machine learning projects with NetworkX, PuLP, and linalg. Packt Publishing Ltd.
	8. Burkov, A. (2019). The hundred-page machine learning book (Vol. 1). Quebec City, QC, Canada: Andriy Burkov.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	2	15
Homework Assignments	-	-
Presentation	-	-
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	35
Final Exam/Final Jury	1	50
Toplam	4	100

Percentage of Semester Work	50
Percentage of Final Work	50
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Gains the ability to apply the acquired knowledge in mathematics, science and engineering.
2	Gains the ability to identify, formulate and solve complex engineering problems			X
3	Gains the ability to accomplish the integration of systems.
4	Gains the ability to design, develop, implement and improve complex systems, components, or processes.
5	Acquires the ability to select,develop and use suitable modern engineering techniques and tools.			X
6	Gains the ability to design/conduct experiments and collect, analyze, and interpret data.		X
7	Gains the ability to function independently and in teams.
8	Gains the ability to make use of oral and written communication skills effectively.
9	Gains the ability to recognize the need for and engage in life-long learning.
10	Attains the ability to understand and exercise professional and ethical responsibility.
11	Gains the ability to understand the impact of engineering solutions.
12	Cultivates the ability to have knowledge of contemporary issues.

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)	16	3	48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	14	4	56
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics	2	5	10
Prepration of Midterm Exams/Midterm Jury	1	5	5
Prepration of Final Exams/Final Jury	1	6	6
Total Workload			125