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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| INDUSTRIAL ENGINEERING (MASTER) (WITH THESIS) | |
| Code | EM567 |
| Name | Data Science and Analytical Methods |
| Term | 2026-2027 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. ALİ KOKANGÜL |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to enable students to comprehensively understand the end-to-end data science process and to effectively apply analytical methods in the stages of data collection, cleaning, analysis, modeling, and interpretation of results.
Course Content
Within this course, fundamental concepts of data science, data preprocessing techniques, exploratory data analysis, statistical modeling, machine learning methods, and data visualization are covered.
Course Precondition
None
Resources
Lecture Notes
Notes
Lecture Book
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains and applies the data science process. |
| LO02 | Performs exploratory data analysis. |
| LO03 | Uses classification and clustering techniques. |
| LO04 | Evaluates model performance. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Conducts scientific research in industrial engineering, understands, interprets and applies knowledge in his/her field domain both in-depth and in-breadth. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquires detailed knowledge for methods and tools of industrial engineering and their limitations. | 4 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Keeps up with the recent changes and applications in the field of Industrial Engineering and examines and learns these innovations when necessary. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | Identifies, gathers and uses necessary information and data. | |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Has the ability to develop/propose new and/or original ideas and methods, propose new solutions for designing systems, components or processes. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Designs Industrial Engineering problems, develops new methods to solve the problems and applies them. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and performs analytical modeling and experimental research and analyze/solves complex matters emerged in this process. | 4 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Works in multi-disciplinary teams, take a leading role and responsibility and develop solutions for complex problems. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Completes and applies the knowledge by using limited resources in scientific methods and integrates the knowledge in the field with the knowledge form various disciplines. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses a foreign language in verbal and written communication at least B2 level of European Language Portfolio. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Presents his/her research findings systematically and clearly in oral or written forms in national and international platforms. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Understands social and environmental implications of engineering practice. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Considers social, scientific and ethical values in data collection, interpretation and announcement processes and professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Science | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 2 | Data Types and Sources | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 3 | Data Preprocessing | Reading Lecture notes | Öğretim Yöntemleri: Anlatım |
| 4 | Exploratory Data Analysis | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Statistical Foundations | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Regression Analysis | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Model Evaluation | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | classical exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Classification Methods | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 10 | Advanced Classification | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Clustering Methods | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Dimensionality Reduction | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Data Visualization | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Project Development | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 15 | Project Development-2 | Reading lecture notes | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Classical exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Classical exam | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||