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 Bachelor’s Degree (First Cycle)
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 Adequate knowledge in mathematics, science and subjects specific to the computer engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems.
2 The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. X
4 The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in computer engineering applications; the ability to utilize information technologies effectively. X
5 The ability to design experiments, conduct experiments, gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the computer engineering discipline. X
6 The ability to work effectively in inter/inner disciplinary teams; ability to work individually
7 Effective oral and writen communication skills in Turkish; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
8 The knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and to receive clear and understandable instructions.
9 Recognition of the need for lifelong learning; the ability to access information, to follow recent developments in science and technology.
10 The ability to behave according to ethical principles, awareness of professional and ethical responsibility;
11 Knowledge of the standards utilized in software engineering applications
12 Knowledge on business practices such as project management, risk management and change management; X
13 Awareness about entrepreneurship, innovation
14 Knowledge on sustainable development
15 Knowledge on the effects of computer engineering applications on the universal and social dimensions of health, environment and safety;
16 Awareness of the legal consequences of engineering solutions
17 An ability to describe, analyze and design digital computing and representation systems. X
18 An ability to use appropriate computer engineering concepts and programming languages in solving computing problems.

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