Optimization in Data Analytics (IE441) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Optimization in Data Analytics	IE441	Area Elective	3	0	0	3	5

Pre-requisite Course(s)
N/A

Course Language	English
Course Type	Elective Courses
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Question and Answer.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	The objective of this course is to introduce different application areas of continuous and discrete optimization techniques with a special focus on data analytics. During the course, foundational concepts in linear, integer, mixed-integer, and non-linear programming models will be applied aligned with fundamental machine learning and statistical modeling techniques to answer questions from engineering and social sciences.
Course Learning Outcomes	The students who succeeded in this course; Ability to understand the role of optimization in data analytics problems. Ability to apply optimization techniques to different domains. Ability to understand similarities and differences of data analytics tools. Ability to use software for computing and visualization with a focus on data analytics applications. Ability to research for a real case study and develop applicable solutions.
Course Content	The concept of linear algebra, probability, linear programming, integer programming, mixed-integer programming, and non-linear programming applications in data analytics such as regression, classification, neural networks; introduction to Python programming and using different Python programming packages to solve data analytics problems.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	First meeting - Syllabus introduction
2	Linear algebra and probability review
3	Linear algebra and probability review
4	Linear algebra and probability review
5	Linear algebra and probability review
6	Integer and mixed-integer programming applications
7	Integer and mixed-integer programming applications
8	Integer and mixed-integer programming applications
9	Midterm Exam
10	Non-linear programming applications
11	Non-linear programming applications
12	Non-linear programming applications
13	Neural networks
14	Neural networks
15	Neural networks
16	Course review

Sources

Course Book	1. Mathematics for Machine Learning, M.P. Deisenroth, A.A. Faisal, C.S. Ong, Cambridge University Press, 2020.
Other Sources	2. A.C. Müller, S. Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, 1 st Edition, O'Reilly Media, 2016.

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	-	-
Presentation	1	15
Project	1	25
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	1	25
Final Exam/Final Jury	1	35
Toplam	4	100

Percentage of Semester Work
Percentage of Final Work	100
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	Adequate knowledge of mathematics, physical sciences and the subjects specific to engineering disciplines; the ability to apply theoretical and practical knowledge of these areas in the solution of complex engineering problems.
2	The ability to define, formulate, and solve complex engineering problems; the ability to select and apply proper analysis and modeling methods for this purpose.
3	The ability to design a complex system, process, device or product under realistic constraints and conditions in such a way as to meet the specific requirements; the ability to apply modern design methods for this purpose.
4	The ability to select, and use modern techniques and tools needed to analyze and solve complex problems encountered in engineering practices; the ability to use information technologies effectively.
5	The ability to design experiments, conduct experiments, gather data, and analyze and interpret results for investigating complex engineering problems or research areas specific to engineering disciplines.
6	The ability to work efficiently in inter-, intra-, and multi-disciplinary teams; the ability to work individually.
7	Effective oral and written communication skills; The knowledge of, at least, one foreign language; the ability to write a report properly, understand previously written reports, prepare design and manufacturing reports, deliver influential presentations, give unequivocal instructions, and carry out the instructions properly.
8	Recognition of the need for lifelong learning; the ability to access information, follow developments in science and technology, and adapt and excel oneself continuously.
9	Acting in conformity with the ethical principles; professional and ethical responsibility and knowledge of the standards employed in engineering applications.
10	Knowledge of business practices such as project management, risk management, and change management; awareness of entrepreneurship and innovation; knowledge of sustainable development.
11	Knowledge of the global and social effects of engineering practices on health, environment, and safety issues, and knowledge of the contemporary issues in engineering areas; awareness of the possible legal consequences of engineering practices.

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	2	28
Presentation/Seminar Prepration	1	4	4
Project	1	20	20
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	1	10	10
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			125