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 6. Semester 3 0 0 3 5
Pre-requisite Course(s)
CMPE341
Course Language English
Course Type Compulsory Departmental Courses
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

Sources

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 Gain sufficient knowledge in mathematics, science and computing; be able to use theoretical and applied knowledge in these areas to solve engineering problems related to information systems.
2 To be able to identify, define, formulate and solve complex engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. X
3 Designs a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. X
4 To be able to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in information systems engineering applications; to be able to use information technologies effectively. X
5 Designs and conducts experiments, collects data, analyzes and interprets results to investigate complex engineering problems or research topics specific to the discipline of information systems engineering. X
6 Can work effectively in disciplinary and multidisciplinary teams; can work individually.
7 a. Communicates effectively both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. b. Knows at least one foreign language.
8 To be aware of the necessity of lifelong learning; to be able to access information, to be able to follow developments in science and technology and to be able to renew himself/herself continuously.
9 a. Acts in accordance with the principles of ethics, gains awareness of professional and ethical responsibility. b. Gains knowledge about the standards used in information systems engineering applications.
10 a. Gains knowledge about business life practices such as project management, risk management and change management. b. Gains awareness about entrepreneurship and innovation. c. Gains knowledge about sustainable development.
11 a. To be able to acquire knowledge about the universal and social effects of information systems engineering applications on health, environment and safety and the problems of the era reflected in the field of engineering. b. Gains awareness of the legal consequences of engineering solutions.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 5 80
Presentation/Seminar Prepration
Project 1 20 20
Report
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