Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty Of Computer And Information Technologies
  • Information Systems And Technologies (English)
  • Course Structure Diagram with Credits
  • Data Mining
  • Description
  • Description
  • Learning Outcomes
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  • ECTS Credit Load

Course Introduction Information
Code - Course Title BST3001 - Data Mining
Course Type Required Courses
Language of Instruction İngilizce
Laboratory + Practice 3+0
ECTS 4.0
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ NİHAL İNCE
Mode of Delivery Face-to-face education. Theoretical lectures, case studies, computer-based practical applications, and project work.
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended Statistics and Probability, Data Structures and Algorithms, Database Management Systems, Python Programming.
Required or Recommended Resources 1. Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. 3rd Edition, Morgan Kaufmann.2. Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Pearson.3. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. 4th Edition, Morgan Kaufmann.
Recommended Reading List
Assessment methods and criteria Midterm Exam: 25%Homework: 15%Project: 20%Final Exam: 40%
Work Placement No mandatory work placement is required.
Sustainability Development Goals Quality Education , Decent Work and Economic Growth , Industry, Innovation and Infrastructure

Content

Weeks Topics
Week - 1 Introduction to Data Mining and Knowledge Discovery in Databases (KDD) Process
Week - 2 Data Warehouse Architecture and Basic Concepts
Week - 3 Data Preprocessing Processes 1: Data Cleaning Algorithms
Week - 4 Data Preprocessing Processes 2: Data Integration, Transformation, and Reduction
Week - 5 Exploratory Data Analysis (EDA): Basic Statistical Summaries
Week - 6 Data Visualization Techniques and Tools
Week - 7 Data Visualization Techniques and Tools
Week - 8 Association Rule Extraction: Frequent Itemsets and Market Basket Analysis
Week - 9 Association Rule Algorithms: Apriori Algorithm Applications
Week - 10 Clustering Analysis 1: Partitioning Methods and K-Means Clustering
Week - 11 Clustering Analysis 2: Hierarchical Clustering and Density-Based Methods
Week - 12 Anomaly (Outlier) Detection and Distance-Based Approaches
Week - 13 Data Mining Applications: Web Mining and Text Mining
Week - 14 Introduction to Big Data Systems

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Team/Group Work
  • Demonstration
  • Drill - Practise
  • Case Study
  • Problem Solving
  • Brain Storming
  • Report Preparation and/or Presentation
  • Proje Design/Management
  • Competences
  • Productive
  • True to core values
  • Rational
  • Questoning
  • Creative
  • Effective use of a foreign language
  • Work in teams
  • Use time effectively
  • Eleştirel düşünebilme
  • Abstract analysis and synthesis
  • Problem solving
  • Applying theoretical knowledge into practice
  • Information Management
  • To work autonomously
  • Organization and planning
  • Elementary computing skills
  • Decision making
  • Project Design and Management

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 25
Homework 1 15
Project 1 20
Final Exam 1 40
Toplam (%) 100
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