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	Knowledge of mathematics, natural sciences, engineering fundamentals, computing, and topics specific to the relevant engineering discipline; the ability to use this knowledge in the solution of complex engineering problems.
2	The ability to identify, formulate, and analyze complex engineering problems using knowledge of basic sciences, mathematics, and engineering, and considering the UN Sustainable Development Goals relevant to the problem.
3	The ability to design creative solutions for complex engineering problems; the ability to design complex systems, processes, devices, or products to meet current and future requirements, considering realistic constraints and conditions.
4	The ability to select and use appropriate techniques, resources, and modern engineering and IT tools, including prediction and modeling, for the analysis and solution of complex engineering problems, with an awareness of their limitations.
5	The ability to use research methods for the investigation of complex engineering problems, including literature search, designing and conducting experiments, collecting data, and analyzing and interpreting results.
6	Knowledge of the effects of engineering practices on society, health and safety, the economy, sustainability, and the environment within the scope of the UN Sustainable Development Goals; awareness of the legal consequences of engineering solutions.
7	Acting in accordance with engineering professional principles, knowledge of ethical responsibility; awareness of acting impartially without discrimination on any grounds and being inclusive of diversity.
8	The ability to work effectively individually and in intra-disciplinary and multi-disciplinary teams (face-to-face, remote, or hybrid) as a team member or leader.
9	"The ability to communicate effectively orally and in writing on technical topics, considering the various differences of the target audience (such as education, language, profession).
10	Knowledge of practices in business life such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation.
11	The ability to engage in life-long learning, including independent and continuous learning, adapting to new and emerging technologies, and thinking inquisitively regarding technological changes.

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