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)
IE202

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	Gains sufficient knowledge in subjects specific to mathematics, natural sciences, and engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields to solve complex engineering problems.
2	Defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose.
3	Designs a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods.
4	Selects and uses modern techniques and tools necessary for analyzing and solving complex problems encountered in engineering applications; gains the ability to use information technologies effectively.
5	Designs experiments, conducts experiments, collects data, and analyzes and interprets the results for studying complex engineering problems or research topics specific to engineering disciplines.
6	Works effectively in both disciplinary and multidisciplinary teams; gains the ability to work individually.
7	Develops effective oral and written communication skills; acquires proficiency in at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear and understandable instructions.
8	Develops awareness of the necessity of lifelong learning; gains access to information, follows developments in science and technology, and continuously renews oneself.
9	Acts in accordance with ethical principles, takes professional and ethical responsibility, and possesses knowledge of standards used in engineering applications.
10	Gains knowledge of business practices such as project management, risk management, and change management; develops awareness of entrepreneurship and innovation; possesses knowledge of sustainable development.
11	Gains knowledge of the impacts of engineering applications on health, environment, and safety in universal and societal dimensions, and the issues reflected in contemporary engineering fields; develops awareness of the legal consequences of engineering solutions.
12	Gains the ability to work in both thermal and mechanical systems fields, including the design and implementation of such systems.

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