COURSE UNIT TITLE

: APPLICATIONS OF DECISION SUPPORT SYSTEMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CME 4432 APPLICATIONS OF DECISION SUPPORT SYSTEMS ELECTIVE 2 2 0 6

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 Online Analytical Processing (OLAP), Customer Relationships Management (CRM) & E-CRM
5 Advanced SQL Queries for Decision making
6 Statistical analysis for Decision making (SPSS)
7 Knowledge Discovery in Databases (selection, preprocessing, transformation, data mining, interpretation).
8 Data mining-I (predictive tasks, descriptive tasks, methods and techniques of data mining)
9 Data mining-II (algorithms and tools of data mining)
10 Applications with data mining algorithms.
11 Stream mining- I (Stream and Big Data, predictive tasks, descriptive tasks, methods and techniques of stream mining)
12 Stream mining-II (algorithms and tools of stream mining)
13 Applications with stream mining algorithms

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
Homeworks

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


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

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

All of the following criteria will be evaluated with exams, homework and laboratory exercises.
1. Learnability of basic concepts will be evaluated with the correct understanding of given problem.
2. The following criteria will be considered during application design composition:
- Correct written and graphical representation of the algorithm
- Including sufficient comments
3. The following criteria should be provided during the translation from application design to program:
- The usage of available algorithms
- The comparisons of similar algorithms in problem solving
4. The following criteria should be considered during the implementation of the program:
- The usage of structured programming techniques
- The usage of sufficient data type
5. Producing significant / correct results of programs that developed for mathematical and other areas will be expected.

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.
3. Instructor reserves the right to quizzes. Notes should be added to these examinations, midterm and final exam grades.

Contact Details for the Lecturer(s)

Prof.Dr. Alp KUT
Dokuz Eylul University
Department of Computer Engineering
Tinaztepe Campus 35160 BUCA/IZMIR
Tel: +90 (232) 301 74 01
e-mail: alp@deu.edu.tr

Office Hours

Monday 15:00 - 17:00
Thursday 9:00 - 12:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Tutorials 13 2 26
Preparations before/after weekly lectures 0
Preparation for midterm exam 1 6 6
Preparation for final exam 1 8 8
Preparation for quiz etc. 0
Preparing assignments 9 8 72
Preparing presentations 0
Other activities within the scope of the atelier pratices 0
1 3 3
Midterm 1 3 3
Quiz etc. 0
TOTAL WORKLOAD (hours) 144

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.14211
LO.25433554
LO.35433554
LO.45433554
LO.55433554

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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