COURSE UNIT TITLE

: APPLICATIONS OF DECISION SUPPORT SYSTEMS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ELECTIVE

Offered By

Computer Engineering

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR YUNUS DOĞAN

Offered to

Computer Engineering

Course Objective

The main objective of this course is to teach the main concepts and components of decision support systems such as descriptions and reasons to be developed by using example applications.

Learning Outcomes of the Course Unit

1   Describe the basic concepts of decision support systems
2   Decision making by developing applications using advanced SQL queries in database management systems
3   Decision making by developing applications using statistical analysis
4   Decision making by developing applications using data mining algorithms
5   Decision making by developing applications using stream mining algorithms

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 The theory of optimal and satisfactory decisions. Decision making under certainty, risk and uncertainty.
2 Expert systems and decision support systems.
3 Creating a knowledge base.
4 Advanced SQL Queries for Decision making
5 Knowledge Discovery in Databases (selection, preprocessing, transformation, data mining, interpretation).
6 Data mining-I (predictive tasks, descriptive tasks, methods and techniques of data mining)
7 Data mining-II (algorithms and tools of data mining)
8 Machine Learning-I
9 Machine Learning-II
10 Applications with data mining algorithms.
11 Optimization Algorithms
12 Stream mining- I (Stream and Big Data, predictive tasks, descriptive tasks, methods and techniques of stream mining)
13 Applications with stream mining algorithms
14 Time Series applications

Recomended or Required Reading

Textbook(s): Turban, Ramesh Sharda, Dursun Delen, "Decision Support and Business Intelligence Systems", Prentice Hall, 9 Edition, 2010
Supplementary Book(s): Jiawei Han, Micheline Kamber and Jian Pei, Data Mining Concepts and Techniques , Third Edition, 2012.

Planned Learning Activities and Teaching Methods

Lectures / Presentation
Guided problem solving
Laboratory exercises
Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 PRJ PROJECT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.20 + PRJ * 0.40 + FIN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + PRJ * 0.40 + RST * 0.40


Further Notes About Assessment Methods

In-semester studies will be evaluated with a midterm exam and a number of laboratory / homework activities.
The final exam will cover all course topics.

Assessment Criteria

Learning outcomes will be evaluated with exams and project.

Language of Instruction

English

Course Policies and Rules

1. Participation is mandatory (%70 theoretical courses and 80% practices)
2. Every cheating attempt will be finalized with disciplinary action.

Contact Details for the Lecturer(s)

Asst.Prof.Dr. Yunus DOĞAN
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 18
e-mail: yunus@deu.edu.tr

Office Hours

Monday 10:30 - 12:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 2 28
Preparation for midterm exam 1 16 16
Preparation for final exam 1 18 18
Project Preparation 1 52 52
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 146

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.142
LO.24443343
LO.34443343
LO.44443343
LO.54443343