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About the Programme Academic Staff Program Qualifications Lessons Matrix of Course & Program Qualifications Turkish Qualifications Framework (TQF) TQF & Program Qualifications
  1. Faculty of Computer and Information Sciences
  2. Department of Data Science and Analytics (English)
  3. Course Structure Diagram with Credits
  4. Data Mining
  5. Description
  • Description
  • Learning Outcomes
  • ECTS Credit Load
  • Course's Contribution to Program
  • Learning Outcomes & Program Qualifications
Last Updated: 22/07/2026

Code - Course Title Course Type Laboratory + Practice ECTS
VBA312 - Data Mining VI. SEMESTER 2 +0 4.0
Language of Instruction İngilizce
Course Type Area Elective Courses
Course Instructor(s) ARAŞ. GÖR. DR. ÖNDER DORAK
Mode of Delivery Face to face
Place of Delivery
Used Educational Platforms
Prerequisites
Courses Recomended
Recommended Resources
Work Placement
Aims of the Course

Weeks Learning Outcomes Topics Teaching Methods
Week - 1 Knowledge Discovery in Databases (KDD) process
Week - 2 Performing data integration
Week - 3 Executing complex data transformations
Week - 4 Advanced Predictive Modeling fundamentals
Week - 5 Building ensemble methods
Week - 6 Implementing support vector machines
Week - 7 Introduction to artificial neural network architectures
Week - 8 Pattern Evaluation: Defining interestingness measures
Week - 9 Interpreting complex data patterns
Week - 10 Deploying data mining models into production
Week - 11 Applied Data Mining: Sequential pattern mining
Week - 12 Text mining applications in data mining
Week - 13 Addressing data mining ethics
Week - 14 Data privacy concerns and regulations

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Assessment Method and Passing Requirements
Quamtity Percentage (%)
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