| Teaching Staff
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| Language of Instruction |
Türkçe (Turkish) |
| Type Of Course |
Compulsory |
| Prerequisites |
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| Recommended Optional Programme Component |
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| Course Objectives |
The aim of this course is to give students theoretical knowledge about data mining algorithms and techniques and to provide students with the ability to select and apply data mining techniques suitable for different applications. This course, students; data preprocessing, togetherness rule analysis, classification and estimation and its applications and to learn cluster analysis. |
| Course Content |
Extract information from internal and external sources to support automated data analysis and organizational decision making processes. Research different applications, methodologies, techniques and models. Classification, Decision Trees, Association Rules, Clustering. This course ends with large data sets from real life, case analysis using Weka Data Mining software. |
| Learning Outcomes (LO) |
1 To be able to define basic data mining concepts
2 To be able to apply data pre-processing
3 To be able to determine the appropriate data mining technique to solve a particular problem
4 To be able to design a data mining model
5 To be able to apply a data mining algorithm
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| Mode of Delivery |
Face to face |
| Course Outline |
| Week |
Topics |
| 1. Week |
Data Mining Introduction |
| 2. Week |
Detailed View of Data Mining |
| 3. Week |
Data Preparation (Data Integration, Reduction, Pre-processing and Cleaning, Conversion) |
| 4. Week |
Discovering Frequent Patterns, Association Rules and Correlations |
| 5. Week |
Row Pattern Analysis |
| 6. Week |
Classification and Forecasting |
| 7. Week |
Clustering |
| 8. Week |
Midterm |
| 9. Week |
Anomaly Detection |
| 10. Week |
Basic Data Mining Tools |
| 11. Week |
Web Mining |
| 12. Week |
|
| 13. Week |
Text Mining |
| 14. Week |
Protecting Privacy in Data Mining |
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| Assessment |
| |
Percentage(%) |
| Mid-term (%) |
40 |
| Quizes (%) |
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| Homeworks/Term papers (%) |
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| Practice (%) |
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| Labs (%) |
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| Projects/Field Work (%) |
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| Seminars/Workshops (%) |
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| Final (%) |
60 |
| Other (%) |
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| Total(%) |
100 |
|
| Course Book (s) and/or References |
Introduction to Data Mining, Global Edition,2018, Pang-Ning Tan (Eser Sahibi), Michael Steinbach (Eser Sahibi), Vipin Kumar (Eser Sahibi),
Özkan, Y., Veri madenciliği yöntemleri, Papatya, 2008. Silahtaroğlu, G., Kavram ve algoritmalarıyla temel veri madenciliği, Papatya, 2008.
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| Work Placement(s) |
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| The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO) |
| | PQ1 | PQ2 | PQ3 | PQ4 | PQ5 | PQ6 | PQ7 | PQ8 | PQ9 | PQ10 | PQ11 | PQ12 | PQ13 | PQ14 | LO1 | 5 | 4 | | | | | | | | | | | | | LO2 | 5 | 4 | | | 3 | | | | 3 | | | | | | LO3 | 5 | 4 | | | 3 | | | | 3 | | | | | | LO4 | 5 | 4 | | | | | | | | | | | | | LO5 | 5 | 4 | | | | | | | | | | | | |
* Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |