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	Natural & Applied Sciences Master's Degree
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	Attains knowledge through wide and in-depth investigations his/her field and surveys, evaluates, interprets, and applies the knowledge thus acquired.
2	Has a critical and comprehensive knowledge of contemporary engineering techniques and methods of application.
3	By using unfamiliar, ambiguous, or incompletely defined data, completes and utilizes the required knowledge by scientific methods; is able to fuse and make use of knowledge from different disciplines.
4	Has the awareness of new and emerging technologies in his/her branch of engineering profession, studies and learns these when needed.
5	Defines and formulates problems in his/her branch of engineering, develops methods of solution, and applies innovative methods of solution.
6	Devises new and/or original ideas and methods; designs complex systems and processes and proposes innovative/alternative solutions for their design.
7	Has the ability to design and conduct theoretical, experimental, and model-based investigations; is able to use judgment to solve complex problems that may be faced in this process.
8	Functions effectively as a member or as a leader in teams that may be interdisciplinary, devises approaches of solving complex situations, can work independently and can assume responsibility.
9	Has the oral and written communication skills in one foreign language at the B2 general level of European Language Portfolio.
10	Can present the progress and the results of his investigations clearly and systematically in national or international contexts both orally and in writing.
11	Knows social, environmental, health, safety, and legal dimensions of engineering applications as well as project management and business practices; and is aware of the limitations and the responsibilities these impose on engineering practices.
12	Commits to social, scientific, and professional ethics during data acquisition, interpretation, and publication as well as in all professional activities.

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