TÜRKÇE
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Information
| Unit | FACULTY OF ENGINEERING |
| INDUSTRIAL ENGINEERING PR. | |
| Code | ENS305 |
| Name | Data Analytics |
| Term | 2025-2026 Academic Year |
| Semester | 5. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 4 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Label | E Elective |
| 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 primary objective of this course is to develop students’ ability to extract meaningful insights from data through analytical methods; to provide comprehensive instruction in statistical analysis and data visualization techniques; and to enable students to apply data-driven approaches to solving real-world problems.
Course Content
Introduction to Data Analytics. Visualization. Probability and Statistics. Inference and Modeling. Regression. Machine Learning Methods
Course Precondition
None
Resources
Ahmed, M., & Pathan, A. S. K. (2018). Data Analytics: Concepts, Techniques, and Applications. CRC Press. Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. Albright, S. C., & Winston, W. L. (2014). Business analytics: Data analysis & decision making. Nelson Education.
Notes
Supplementary lecture notes will be provided.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Demonstrate an understanding of data collection, cleaning, and preprocessing techniques. |
| LO02 | Apply fundamental statistical analysis methods to real-world datasets |
| LO03 | Effectively communicate analytical results using appropriate data visualization tools |
| LO04 | Employ regression, classification, and clustering techniques to analyze complex data |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Have sufficient knowledge of mathematics, science and related engineering disciplines; can use the theoretical and applied knowledge in these fields in complex engineering problems. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Acquire the ability to identify, define, formulate and solve complex Industrial Engineering problems; for this purpose, will have the ability to choose and apply appropriate analysis and modeling methods. | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; can apply modern design methods for this purpose. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Develops modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications, and has the ability to use information technologies effectively. | 2 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Have the ability to design experiments, collect data, analyze and interpret results for the investigation of complex engineering problems or discipline-specific research topics. | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Have the ability to work effectively in disciplinary and multi-disciplinary teams or individually. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Ability to communicate effectively in Turkish orally and in writing; knowledge of at least one foreign language; have the ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Have the awareness of the necessity of lifelong learning; can follow the developments in science and technology and have the ability to constantly renew themselves. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Acts in accordance with ethical principles, has knowledge about the standards used in engineering applications with the awareness of professional and ethical responsibility. | 3 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Gain knowledge of business practices such as project management, risk management and change management; become aware of entrepreneurship and innovation. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Gains knowledge about the effects of engineering practices on health, environment and safety in universal and social dimensions and the problems of the age reflected in the field of engineering and has awareness of the legal consequences of engineering solutions. | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | They can benefit from the power of effective communication in their professional life and have the ability to interpret developments correctly. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Have the ability to design, develop, implement and improve integrated systems involving machine, time, information and money. | |
| PLO14 | Yetkinlikler - Öğrenme Yetkinliği | Have the ability to design, develop, implement and improve complex products, processes, businesses, systems by applying modern design methods, under realistic conditions and constraints such as cost, environment, sustainability, manufacturability, ethical, health, safety and political issues. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction and Fundamental Concepts | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 2 | Data Collection and Sources | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Data Cleaning and Preprocessing | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Descriptive Statistics | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 5 | Data Visualization | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Correlation and Regression | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 7 | Multiple Regression | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Yazılı Sınav |
|
| 9 | Introduction to Classification Models | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 10 | Cluster Analysis | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 11 | Time Series Analysis | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 12 | Data Ethics and Privacy | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım, Tartışma |
| 13 | Project Work | Review of the Relevant Chapter from the Course Textbook | Öğretim Yöntemleri: Anlatım |
| 14 | Project Presentations I | Data Analysis Execution and Project Presentation Development | Ölçme Yöntemleri: Proje / Tasarım |
| 15 | Project Presentations II | Review of the Relevant Chapter from the Course Textbook | Ölçme Yöntemleri: Proje / Tasarım |
| 16 | Term Exams | Ölçme Yöntemleri: Yazılı Sınav |
|
| 17 | Term Exams | 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 | 1 | 14 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 14 | 14 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 100 | ||
| Total Workload / 25 (h) | 4,00 | ||
| ECTS | 4 ECTS | ||