ECTS - Optimization in Data Analytics

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
1 2 3 4 5
1 Applies knowledge in mathematics, science, and computing to solve engineering problems related to manufacturing technologies.
2 Analyzes and identifies problems specific to manufacturing technologies.
3 Develops an approach to solve encountered engineering problems, and designs and conducts models and experiments.
4 Designs a comprehensive manufacturing system (including method, product, or device development) based on the creative application of fundamental engineering principles, within constraints of economic viability, environmental sustainability, and manufacturability.
5 Selects and uses modern techniques and engineering tools for manufacturing engineering applications.
6 Effectively uses information technologies to collect and analyze data, think critically, interpret, and make sound decisions.
7 Works effectively as a member of multidisciplinary and intra-disciplinary teams or individually; demonstrates the confidence and necessary organizational skills.
8 Communicates effectively in both spoken and written Turkish and English.
9 Engages in lifelong learning, accesses information, keeps up with the latest developments in science and technology, and continuously renews oneself.
10 Demonstrates awareness and a sense of responsibility regarding professional, legal, ethical, and social issues in the field of Manufacturing Engineering.
11 Effectively utilizes resources (personnel, equipment, and costs) to enhance national competitiveness and improve manufacturing industry productivity; conducts solution-oriented project and risk management; and demonstrates awareness of entrepreneurship, innovation, and sustainable development.
12 Considers the health, environmental, social, and legal consequences of engineering practices at both global and local scales when making decisions.

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