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 | 4 | 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
Veri madenciliği kavramları, Veri önişleme, Temel bileşen analizi, Kümeleme, Sınıflandırma, Kestirim, K-en yakın komşu algoritması, Karar ağaçları, Yapay sinir zekaları, Birliktelik kuralları
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 | Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex problems or discipline-specific research topics in the field of Industrial Engineering | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Veri madenciliği tanımı, süreçlerini ve uygulama örneklerin öğrenir. | P.5.23 | 1 |
| O2 | Temel veri madenciliği tekniklerini veri setlerinde uygulayabilir. | P.5.24 | 1 |
| O3 | Kullanılan tekniklerin sonuçlarını doğru bir şekilde analiz eder ve model performansını değerlendirir. | P.5.25 | 1 |
| O4 | Veri madenciliği konularında karşılaşılan veri güvenliği ve etik kurallar hakkında bilgi sahibi olurlar. | 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 | Veri madenciliği tanımı, süreçlerini ve uygulama örneklerin öğrenir. | 5 |
| O2 | Temel veri madenciliği tekniklerini veri setlerinde uygulayabilir. | 5 |
| O3 | Kullanılan tekniklerin sonuçlarını doğru bir şekilde analiz eder ve model performansını değerlendirir. | 5 |
| O4 | Veri madenciliği konularında karşılaşılan veri güvenliği ve etik kurallar hakkında bilgi sahibi olurlar. | 5 |