Industrial Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15271740 | Data Mining | 2025 | Autumn | 7 | 3+0+0 | 0 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Murat DARÇIN |
| Instructor(s) | - |
| Instructor Assistant(s) | - |
Course Content
Data mining concepts, Data preprocessing, Principal component analysis, Clustering, Classification, Estimation, K-nearest neighbor algorithm, Decision trees, Artificial neural intelligence, Association rules
Objectives of the Course
Develop the ability to find patterns and regularities embedded in large data sets and extract useful information from raw data.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P5 | The ability to use research methods, including literature review, experimental design, experiment execution, data collection, analysis, and interpretation of results, to investigate complex industrial engineering problems. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Learn the definition of data mining, its processes, and application examples. | P.5.23 | 1 |
| O2 | Apply basic data mining techniques to datasets. | P.5.24 | 1 |
| O3 | Analyze the results of applied techniques and evaluate model performance. | P.5.25 | 1 |
| O4 | Demonstrate knowledge of data security and ethical principles in data mining. | P.5.26 | 1 |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to Data Mining |
| 2 | Data Types/Data Quality/Data preprocessing |
| 3 | Similarity measures/Data discovery |
| 4 | Classification – Basic Concepts/Decision Trees |
| 5 | Classification – Near Neighbor Classifier/Naive Bayes |
| 6 | Classification – Artificial Neural Networks |
| 7 | Classification – Support Vector Machine/Classification Performance Evaluation |
| 8 | Midterm Exam |
| 9 | Clustering - Core Concepts/Centre-Based Clustering |
| 10 | Clustering - Hierarchical Clustering |
| 11 | Clustering – Density Based Clustering/Clustering Performance Evaluation |
| 12 | Association Analysis |
| 13 | Simple and Multiple Linear Regression |
| 14 | Basit ve Çoklu Doğrusal Regresyon |
| 15 | Preparation for the fianl exam |
Textbook or Material
| Resources | Shumeli, G., Patel, N.R., Bruce, P.C. (2012). Data Mining for Business Intelligence: Concepts, Techniques and Application in Microsoft Excel with XLMiner. E & B Plus. |
| Shumeli, G., Patel, N.R., Bruce, P.C. (2012). Data Mining for Business Intelligence: Concepts, Techniques and Application in Microsoft Excel with XLMiner. E & B Plus. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 40 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 30 | 30 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 36 | 36 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P5 |
|---|---|---|
| O1 | Learn the definition of data mining, its processes, and application examples. | 5 |
| O2 | Apply basic data mining techniques to datasets. | 5 |
| O3 | Analyze the results of applied techniques and evaluate model performance. | 5 |
| O4 | Demonstrate knowledge of data security and ethical principles in data mining. | 5 |