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 Lecturer(s) |
|
| 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;
|
| 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 | Acquires sufficient knowledge in mathematics, natural sciences, and related engineering disciplines; gains the ability to use theoretical and applied knowledge in these fields in solving complex engineering problems. | |||||
| 2 | Gains the ability to identify, define, formulate, and solve complex engineering problems; acquires the skill to select and apply appropriate analysis and modeling methods for this purpose. | X | ||||
| 3 | Gains the ability to design a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions, and applies modern design methods for this purpose. | |||||
| 4 | Develops the skills to develop, select, and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; gains the ability to effectively use information technologies. | X | ||||
| 5 | Gains the ability to design experiments, conduct experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics. | X | ||||
| 6 | Acquires the ability to work effectively in intra-disciplinary and multidisciplinary teams, as well as individual work skills. | |||||
| 7 | Acquires effective oral and written communication skills in Turkish; at least one foreign language proficiency; gains the ability to write effective reports, understand written reports, prepare design and production reports, make effective presentations, and give and receive clear instructions. | |||||
| 8 | Develops awareness of the necessity of lifelong learning; gains the ability to access information, follow developments in science and technology, and continuously renew oneself. | |||||
| 9 | Acquires the consciousness of adhering to ethical principles, and gains professional and ethical responsibility awareness. Gains knowledge about the standards used in industrial engineering applications. | |||||
| 10 | Gains knowledge about practices in the business life such as project management, risk management, and change management. Develops awareness about entrepreneurship and innovation. Gains knowledge about sustainable development. | |||||
| 11 | Gains knowledge about the universal and social dimensions of the impacts of industrial engineering applications on health, environment, and safety, as well as the problems reflected in the engineering field of the era. Gains awareness of the legal consequences of engineering solutions. | |||||
| 12 | Gains skills in the design, development, implementation, and improvement of integrated systems involving human, material, information, equipment, and energy. | |||||
| 13 | Gains knowledge about appropriate analytical and experimental methods, as well as computational methods, for ensuring system integration. | |||||
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 | ||