COURSE UNIT TITLE

: DATA MINING AND BUSINESS INTELLIGENCE APPLICATIONS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ISY 5048 DATA MINING AND BUSINESS INTELLIGENCE APPLICATIONS COMPULSORY 3 0 0 4

Offered By

Management Science

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR GÜZIN ÖZDAĞOĞLU

Offered to

Management Science
Quantitative Methods

Course Objective

This course aims at identifying the concepts and models of data mining and its applications in business intelligence; experiencing software and applications that are used especially in present-day businesses.

Learning Outcomes of the Course Unit

1   To be able to identify the concepts of data mining and business intelligence.
2   To be able to understand the importance of decision support that is provided by the concepts of data mining and business intelligence.
3   To be able to comprehend basic models and algorithms used in data mining.
4   To be able to develop a model that is compatible with the desired decision support.
5   To be able to distinguish an appropriate algorithm for the developed decision model.
6   To be able to apply data mining algorithms through a particular software in the direction of the targeted decision making process.
7   To be able to interpret the information obtained from applications in the way of decision making process by discovering the knowledge underlying the findings.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction , General Definition of Data Mining, Review of Basic Knowledge for Data Analysis
2 Basic Concepts of Data Mining and Business Intelligence Application Areas of Data Mining and BI
3 Data Mining Models and Basic Functions
4 Data Pre-processing
5 Data Summarization and Reduction
6 Data Warehouses and Their Properties
7 Data Cube Calculations and OLAP
8 Classifying Algorithms Based on Decision Trees
9 Clustering Algorithms Association Rules
10 Other Classification Models
11 Special Applications (text mining, web mining, etc.)
12 Project Presentations
13 Project Presentations
14 Conclusion and Evaluation

Recomended or Required Reading

Textbooks:
1. Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Pearson Addison Wesley.
2. Data Mining: Concepts and Techniques. J. Han, M. Kamber. Morgan Kaufmann Publishers.
Secondary References:
1. Kavram Ve Algoritmalarıyla Temel Veri Madenciliği. Gökhan Silahtaroğlu. Papatya Yayınları.
2. Veri Madenciliği Yöntemleri. Yalçın Özkan. Papatya Yayınları.
3. Applied Data Mining, Statistical Methods for Business and Industry. Paolo Giudici. Wiley Press.

Other references for case studies and important papers will actually be shared with students during lectures.

Planned Learning Activities and Teaching Methods

Short theoretical lectures, interactive applications, case studies, and projects will be handled during lessons. The details about course activities are explained in the parts: Assessment methods and Workload calculation .

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40


Further Notes About Assessment Methods


Assessment Criteria

1. Basic concepts and techniques about data mining methods and concepts will be measured through the performance level in class discussions and exams.
2. Assessment based on comprehending and applying the appropriate model will be conducted through responses of application questions in exams and performance on
in class applications.
3. Assessments based on interpretation of findings and results will be carried out through performances in the case studies and project presentations.

Language of Instruction

Turkish

Course Policies and Rules

1. Attending to lectures is mandatory.
2. Students are expected to obey the general principles of scientific research and ethic. .
3. In clas applications will be handled in parallel with lectures.

Contact Details for the Lecturer(s)

Asst.Prof.Dr. Güzin Özdağoğlu
DEU Faculty of Business, Department of Business Administration, Division of Quantitative Methods.
Office No: 122 Tel: (232) 3018252 E-mail: guzin.kavrukkoca@deu.edu.tr
Personal web page: http://kisi.deu.edu.tr/guzin.kavrukkoca

Office Hours

Office hours of the instructor will be announced in the first lesson.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Case study 14 1 14
Preparation for midterm exam 1 12 12
Preparation for final exam 1 10 10
Preparing assignments 2 15 30
Preparing presentations 1 3 3
Midterm 1 1 1
Final 1 1 1
TOTAL WORKLOAD (hours) 99

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.1
LO.2
LO.3
LO.4
LO.5
LO.6
LO.7

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

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