Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
HRT435 Data Mining (EC 5) 927006 4 7 3
Level of Course Unit
First Cycle
Objectives of the Course
Introducing data mining and disseminating its use, Gaining the ability to analyze large-scale databases
Name of Lecturer(s)
Learning Outcomes
  1. To be able to use classification algorithms
  2. Use bundling algorithms
  3. To be able to use bin analysis
  4. To be able to use data mining software
Mode of Delivery
Formal Education
Prerequisites and co-requisities
Recommended Optional Programme Components
Recommended or Required Reading
• Gökhan Silahtaroğlu, Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Papatya Yayıncılık (2008)
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley,(2005).
Planned Learning Activities and Teaching Methods
Language of Instruction
Work Placement(s)
Course Contents
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Introduction and General Definitions
2.Applications of Data Mining
3.Introducing ready-made programs used in data mining - Data mining in spreadsheet programs
4.Preparing the data for analysis (steps)
6.Classification and Clustering
7.Decision Trees
8.Statistics in Data Mining
10.Artificial Intelligence in Data Mining
11.Artificial Neural Networks in Data Mining
12.Association Rules
13.Other Mining Techniques in Data Mining-Web and Text Mining
14.Sample Applications
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination1100
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination122
Final Examination122
Self Study14114
Individual Study for Mid term Examination14228
Individual Study for Final Examination14228