ECTS - Data Warehousing and Mining

Data Warehousing and Mining (ISE314) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Data Warehousing and Mining ISE314 3 0 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The objectives of this course are to introduce and describe data warehousing steps and methods for accessing and analyzing warehouse data; and to introduce the basic concepts and rule mining techniques and develop skills of using recent data mining software for solving practical problems.
Course Learning Outcomes The students who succeeded in this course;
  • Manage effective use of data stored in relational databases
  • Create a clean, consistent repository of data within a data warehouse
  • Utilise various levels and types of summarisation of data to support management decision making
  • Discover patterns and knowledge that is embedded in the data using several different data mining techniques, such as neural nets, decision trees and associative rule mining
Course Content Data warehousing fundamentals, planning, design and implementation and administration of data warehouses, data cube computation, OLAP query processing; fundamentals of data mining and relationship with data warehouse and OLAP systems; association rule mining; algorithms for clustering, classification and rule learning.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction to data warehousing Chapter 1,2 (Textbook 1)
2 Dimensional data modeling Chapter 2 (Textbook 2)
3 Building the data warehouse 1 Chapter 6 (Textbook 1)
4 Building the data warehouse 2 Chapter 6 (Textbook 1)
5 Building the data warehouse 3 Chapter 6 (Textbook 1)
6 Data mining and data visualization 1 Chapter 3 (Textbook 1)
7 Data mining and data visualization 2 Chapter 3 (Textbook 1)
8 Data mining techniques: Clustering 1 Chapter 5 (Other sources 3)
9 Data mining techniques: Decision trees 3 Chapter 5 (Other sources 3)
10 Practical data warehousing and data mining 1 Applications on software
11 Practical data warehousing and data mining 2 Applications on software
12 Practical data warehousing and data mining 3 Applications on software
13 Practical data warehousing and data mining 4 Applications on software
14 Practical data warehousing and data mining 5 Applications on software
15 Final Examination Period Review of topics
16 Final Examination Period Review of topics


Course Book 1. George M. Marakas, “Modern Data Warehousing, Mining, and Visualization: Core Concepts”, Prentice Hall, 2003.
2. R. Kimball and M. Ross, “The Data Warehouse Toolkit” , 2002, Wiley
Other Sources 3. Han J.W., Kamber M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006.
4. Tan P.N., Steinbach M., Kumar V. Introduction to Data Mining. Addison Wesley, 2005.
5. Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply knowledge of mathematics, science, and engineering.
2 An ability to design and conduct experiments, as well as to analyse and interpret data.
3 An ability to design a system, component, or process to meet desired needs.
4 An ability to function on multi-disciplinary domains.
5 An ability to identify, formulate, and solve engineering problems.
6 An understanding of professional and ethical responsibility.
7 An ability to communicate effectively.
8 Recognition of the need for, and an ability to engage in life-long learning.
9 A knowledge of contemporary issues.
10 An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.
11 Skills in project management and recognition of international standards and methodologies
12 An ability to produce engineering products or prototypes that solve real-life problems.
13 Skills that contribute to professional knowledge.
14 An ability to make methodological scientific research.
15 An ability to produce, report and present an original or known scientific body of knowledge.
16 An ability to defend an originally produced idea.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Special Course Internship
Field Work
Study Hours Out of Class 16 5 80
Presentation/Seminar Prepration
Project 1 20 20
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 15 15
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 135