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 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
1 2 3 4 5
1 Having accumulated knowledge on mathematics, science and engineering and an ability to apply these knowledge to solve Civil engineering problems. X
2 Ability to design Cİvil Engineering systems fulfilling sustainability in environment and manufacturability and economic constraints
3 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems.
4 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment
5 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications X
6 Ability to carry out independent research in the field and to report the results of the research effectively and be able to present the research results at scientific meetings.
7 Sufficient oral and written English knowledge to follow scientific conferences in the field and communicate with colleagues.
8 Ability to effectively use knowledge in the field to work in disciplinary/multidisciplinary teams and the skill to lead these teams X
9 Consciousness on the necessity of improvement and sustainability as a result of life-long learning,ability for continuous renovation and monitoring the developments on science and technology and awareness on entrepreneurship and innovation
10 Professional and ethical responsibility to gather and interpret data, apply and announce solutions to Civil Engineering problems.
11 An ability to investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary.

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