Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| BUSINESS INTELLIGENCE and DATA MINING | BUSD1213566 | Spring Semester | 3+0 | 3 | 9 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Name of Lecturer(s) | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Assistant(s) | |
| Aim | To provide students with research skills to create a data warehouse from databases, use OLAP and data mining models on these data warehouses, and to bring them to the level so that they can write data mining algorithms. |
| Course Content | This course contains; Introduction,Data warehouse and OLAP,Data Preparation for data analysis , data cleaning noise reduction,Data mining task analysis problem description,Clustering and Partitioned Algorithms,Classification Statistics based algorithms,Classification,Decision Trees,Fraud Detection,Association Analysis,Implementation data mining business applications with computer software,Text Mining - Social Media Data Fetching and Processing,Genetic Algorithms and Fuzzy Logic App.,Artificial Neural Networks. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Produces data warehouse from database. | 9 | D |
| 1.1. Explains datamining | ||
| 1.2. Defines Data Warehouse. | ||
| 2. Relates Data Mining Models to each other. | 6, 9 | E |
| 2.1. Explains data mining models | ||
| 2.2. Defines the concept of classification. | ||
| 2.3 Defines the concept of clustering. | ||
| 2.4. Defines the concept of link analysis. | ||
| 3. Applies the classification model. | 13 | D |
| 3.1. Defines supervised learning. | ||
| 3.2. Determines the concept of class. | ||
| 3.3. Lists statistical algorithms. | ||
| 3.4. Implements decision trees. | ||
| 3.5. Defines decision tree algorithms. | ||
| 4. Employs the clustering model. | 14, 19 | E |
| 4.1. Defines unsupervised learning. | ||
| 4.2. Explains the concept of clustering. | ||
| 4.3. Sorts clustering algorithms. | ||
| 4.4. Applies K-means algorithm. | ||
| 4.5. Defines genetic algorithms. | ||
| 5. Employs the connection analysis model. | 6 | H |
| 5.1. Interprets connection analysis rules. | ||
| 5.2. Explains the concept of leverage. | ||
| 5.3. Applies the relationship analysis method. | ||
| 6. Employs Data Mining Algorithms. | 12 | A, F |
| 6.1. Employs classification algorithms on data. | ||
| 6.2. Employs clustering algorithms on data. |
| Teaching Methods: | 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 19: Brainstorming Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, H: Performance Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction | |
| 2 | Data warehouse and OLAP | |
| 3 | Data Preparation for data analysis , data cleaning noise reduction | |
| 4 | Data mining task analysis problem description | |
| 5 | Clustering and Partitioned Algorithms | |
| 6 | Classification Statistics based algorithms | |
| 7 | Classification | |
| 8 | Decision Trees | |
| 9 | Fraud Detection | |
| 10 | Association Analysis | |
| 11 | Implementation data mining business applications with computer software | |
| 12 | Text Mining - Social Media Data Fetching and Processing | |
| 13 | Genetic Algorithms and Fuzzy Logic App. | |
| 14 | Artificial Neural Networks |
| Resources |
| Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Defines theoretical knowledge in the field of management. | X | |||||
| 1 | Uses at least one computer program required in the field of management. | X | |||||
| 1 | Adopts the principles of scientific ethics and scientific responsibility. | X | |||||
| 1 | Uses theoretical and practical knowledge in the field of management. | X | |||||
| 2 | Analyzes and uses basic information and data from different disciplines (economy, finance, sociology, law, business) in order to carry out interdisciplinary studies. | X | |||||
| 2 | Have the research skills required to conduct academic studies. | X | |||||
| 2 | Explains the mathematical and statistical methods required in the field of management. | X | |||||
| 3 | Have time management skills. | X | |||||
| 3 | Expands the boundaries of knowledge in the field by producing or interpreting an original work by conducting at least one scientific study in the field. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 14 | 3 | 42 | |||
| Course Hours | 0 | 0 | 0 | |||
| Guided Problem Solving | 5 | 5 | 25 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 12 | 10 | 120 | |||
| Term Project | 8 | 4 | 32 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 5 | 5 | 25 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 0 | 0 | 0 | |||
| Midterm Exam | 1 | 10 | 10 | |||
| Midterm Exam | 0 | 0 | 0 | |||
| General Exam | 0 | 0 | 0 | |||
| General Exam | 1 | 20 | 20 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 274 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(274/30) | 9 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| BUSINESS INTELLIGENCE and DATA MINING | BUSD1213566 | Spring Semester | 3+0 | 3 | 9 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Third Cycle (Doctorate Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Name of Lecturer(s) | Prof.Dr. Gökhan SİLAHTAROĞLU |
| Assistant(s) | |
| Aim | To provide students with research skills to create a data warehouse from databases, use OLAP and data mining models on these data warehouses, and to bring them to the level so that they can write data mining algorithms. |
| Course Content | This course contains; Introduction,Data warehouse and OLAP,Data Preparation for data analysis , data cleaning noise reduction,Data mining task analysis problem description,Clustering and Partitioned Algorithms,Classification Statistics based algorithms,Classification,Decision Trees,Fraud Detection,Association Analysis,Implementation data mining business applications with computer software,Text Mining - Social Media Data Fetching and Processing,Genetic Algorithms and Fuzzy Logic App.,Artificial Neural Networks. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| 1. Produces data warehouse from database. | 9 | D |
| 1.1. Explains datamining | ||
| 1.2. Defines Data Warehouse. | ||
| 2. Relates Data Mining Models to each other. | 6, 9 | E |
| 2.1. Explains data mining models | ||
| 2.2. Defines the concept of classification. | ||
| 2.3 Defines the concept of clustering. | ||
| 2.4. Defines the concept of link analysis. | ||
| 3. Applies the classification model. | 13 | D |
| 3.1. Defines supervised learning. | ||
| 3.2. Determines the concept of class. | ||
| 3.3. Lists statistical algorithms. | ||
| 3.4. Implements decision trees. | ||
| 3.5. Defines decision tree algorithms. | ||
| 4. Employs the clustering model. | 14, 19 | E |
| 4.1. Defines unsupervised learning. | ||
| 4.2. Explains the concept of clustering. | ||
| 4.3. Sorts clustering algorithms. | ||
| 4.4. Applies K-means algorithm. | ||
| 4.5. Defines genetic algorithms. | ||
| 5. Employs the connection analysis model. | 6 | H |
| 5.1. Interprets connection analysis rules. | ||
| 5.2. Explains the concept of leverage. | ||
| 5.3. Applies the relationship analysis method. | ||
| 6. Employs Data Mining Algorithms. | 12 | A, F |
| 6.1. Employs classification algorithms on data. | ||
| 6.2. Employs clustering algorithms on data. |
| Teaching Methods: | 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 19: Brainstorming Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, H: Performance Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Introduction | |
| 2 | Data warehouse and OLAP | |
| 3 | Data Preparation for data analysis , data cleaning noise reduction | |
| 4 | Data mining task analysis problem description | |
| 5 | Clustering and Partitioned Algorithms | |
| 6 | Classification Statistics based algorithms | |
| 7 | Classification | |
| 8 | Decision Trees | |
| 9 | Fraud Detection | |
| 10 | Association Analysis | |
| 11 | Implementation data mining business applications with computer software | |
| 12 | Text Mining - Social Media Data Fetching and Processing | |
| 13 | Genetic Algorithms and Fuzzy Logic App. | |
| 14 | Artificial Neural Networks |
| Resources |
| Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Defines theoretical knowledge in the field of management. | X | |||||
| 1 | Uses at least one computer program required in the field of management. | X | |||||
| 1 | Adopts the principles of scientific ethics and scientific responsibility. | X | |||||
| 1 | Uses theoretical and practical knowledge in the field of management. | X | |||||
| 2 | Analyzes and uses basic information and data from different disciplines (economy, finance, sociology, law, business) in order to carry out interdisciplinary studies. | X | |||||
| 2 | Have the research skills required to conduct academic studies. | X | |||||
| 2 | Explains the mathematical and statistical methods required in the field of management. | X | |||||
| 3 | Have time management skills. | X | |||||
| 3 | Expands the boundaries of knowledge in the field by producing or interpreting an original work by conducting at least one scientific study in the field. | X | |||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |