YBS 410


Course Title Course Code Program Level
DATA MINING IN BUSINESS YBS 452 Management Information Systems B.A. / B.Sc.

Course Term
(Course Semester)
Teaching and Learning Methods
Credits
Theory Practice Lab Projects/Field Work Seminars/Workshops Other Total Credits ECTS Credits
08
(Spring)
3 3 5

Teaching Staff
Language of Instruction Türkçe (Turkish)
Type Of Course Elective
Prerequisites
Recommended Optional Programme Component
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
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
Assessment
  Percentage(%)
Mid-term (%) 40
Quizes (%)
Homeworks/Term papers (%)
Practice (%)
Labs (%)
Projects/Field Work (%)
Seminars/Workshops (%)
Final (%) 60
Other (%)
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.
Work Placement(s)
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

  

 

  

 

 

 

 

 

 

 

LO2

5

 

 

 3

 

 

 

 3

 

 

 

 

 

LO3

5

 

 

 

 

 

3

 

 

 

 

 

LO4

5

 4

 

 

 

 

 

 

 

 

 

 

 

 

LO5

5

 4

 

 

 

 

 

 

 

 

 

 

 

 

 * Contribution Level : 1 Very low    2 Low     3 Medium     4 High      5 Very High