|
|
| ISTL5053 | Statistics Applied Data Mining | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Tolga BERBER | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course will enable a student to learn data preprocessing, association rule mining, classification and prediction, and cluster analysis with applications. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Define basic data mining concepts | 1,3,5,6 | 1,5 | | PO - 2 : | Apply preprocessing operations on data | 1,3,5,6 | 1,5 | | PO - 3 : | Determine which data mining technique is appropriate to solve a particular problem | 1,3,5,6 | 5,6 | | PO - 4 : | Design a data mining model | 1,3,5,6 | 5,6 | | PO - 5 : | Implement a data mining algorithm | 1,3,5,6 | 5,6 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| In this course, Introduction to Data Mining, Detailed View to Data Mining, Data Preparation (Data Integration, Reduction, Preprocessing and Cleanup, Transformation), Classification and Estimation, Clustering, Anomaly Detection and Basic Data Mining Tools will be examined. |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Data Mining | | | Week 2 | Data Mining: A Closer View | | | Week 3 | Data Preparation (Data Integration, Reduction, Preprocessing and Cleaning, Transformation) | | | Week 4 | Classification with Decision Trees | | | Week 5 | Classification and Regression Trees (CART) | | | Week 6 | Memory-Based Classification: k-Nearest Neighbor Algorithm | | | Week 7 | Cluster Algorithms 1 | | | Week 8 | Cluster Algorithms 2 | | | Week 9 | Midterm Exam | | | Week 10 | Rules of Association | | | Week 11 | Statistical Classification Models: Bayesian Classifiers and Bayesian Networks | | | Week 12 | Optimization Based Classification Models: Support Vector Machine | | | Week 13 | Introduction to Rapidminer | | | Week 14 | Rapidminer Applications | | | Week 15 | Data Mining with R | | | Week 16 | Final Exam | | | |
| 1 | Silahtaroğlu, G. 2013; Veri Madenciliği Kavram ve Algoritmalar, Papatya Yayınları, İstanbul | | | 2 | Özkan Y. 2013; Veri Madenciliği Yöntemleri, Papatya Yayınları, İstanbul | | | |
| 1 | Çelik U., Akçetin, E., Gök, M. 2017, Rapidminer ile Uygulamalı Veri Madenciliği, Pusula Yaınları, İstanbul | | | 2 | Altunkaynak, B. 2017 Veri Madenciliği Yöntemleri ve R Uygulamaları, Seçkin Yayınları, Ankara | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 22/11/2021 | 1,5 | 30 | | Quiz | 12 | 12/12/2021 | 1,5 | 20 | | End-of-term exam | 16 | 10/01/2022 | 1,5 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 2 | 2 | 4 | | Arasınav | 1.5 | 1 | 1.5 | | Kısa sınav | 1.5 | 1 | 1.5 | | Dönem sonu sınavı için hazırlık | 3 | 3 | 9 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 87.5 |
|