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 subjects related to mathematics, natural sciences, and Electrical and Electronics Engineering discipline; ability to apply theoretical and applied knowledge in those fields to the solution of complex engineering problems.			X
2	An ability to identify, formulate, and solve complex engineering problems, ability to choose and apply appropriate models and analysis methods for this.			X
3	An ability to design a system, component, or process under realistic constraints to meet desired needs, and ability to apply modern design approaches for this.			X
4	The ability to select and use the necessary modern techniques and tools for the analysis and solution of complex problems encountered in engineering applications; the ability to use information technologies effectively
5	Ability to design and conduct experiments, collect data, analyze and interpret results for investigating complex engineering problems or discipline-specific research topics.
6	An ability to function on multi-disciplinary teams, and ability of individual working.
7	Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; active report writing and understanding written reports, preparing design and production reports, the ability to make effective presentation the ability to give and receive clear and understandable instructions.
8	Awareness of the necessity of lifelong learning; the ability to access knowledge, follow the developments in science and technology and continuously stay updated.
9	Acting compliant with ethical principles, professional and ethical responsibility, and knowledge of standards used in engineering applications.
10	Knowledge about professional activities in business, such as project management, risk management, and change management awareness of entrepreneurship and innovation; knowledge about sustainable development.
11	Knowledge about the impacts of engineering practices in universal and societal dimensions on health, environment, and safety. the problems of the current age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

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