COURSE UNIT TITLE

: DATA MINING TECHNIQUES IN STATISTIC

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
STA 6015 DATA MINING TECHNIQUES IN STATISTIC ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR EFENDI NASIBOĞLU

Offered to

Industrial Ph.D. Program In Advanced Biomedical Technologies
Computer Science
Industrial Ph.D. Program In Advanced Biomedical Technologies
Biomedical Tehnologies (English)
Statistics
Statistics
Ph.D. in Computer Science
STATISTICS

Course Objective

The goal of this course is to understand and to be able to programmatically apply the basic concepts of data mining. Topics include Data mining theory and algorithms, Programming data mining algorithms, Acquiring, parsing, filtering, mining, representing, refining and interacting with data, Data visualization.

Learning Outcomes of the Course Unit

1   Have a good understanding of basic Data Mining concepts.
2   Have a good understanding about the Data Mining tasks.
3   Have a basic knowledge of current data mining applications.
4   Have ability to use a basic Data Mining software.
5   Have ability to design applications according to CRISP-DM process.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 INTRODUCTION TO DATA MINING
2 DATA PREPROCESSING
3 EXPLORATORY DATA ANALYSIS
4 STATISTICAL APPROACHES TO ESTIMATION AND PREDICTION, ASSIGMENT1
5 k-NEAREST NEIGHBOR ALGORITHM
6 DECISION TREES, ASSIGMENT2
7 NEURAL NETWORKS
8 Project presentations
9 HIERARCHICAL AND k-MEANS CLUSTERING
10 KOHONEN NETWORKS
11 ASSOCIATION RULES
12 MODEL EVALUATION TECHNIQUES, ASSIGMENT3
13 DATA MINING APPLICATIONS
14 DATA MINING APPLICATIONS (continued)

Recomended or Required Reading

Textbook(s):
Larose D., Discovering Knowledge in Data:An introduction to data mining, John Wiley & Sons, Inc., 2005
Supplementary Book(s):
P. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Addison Wesley, 2006

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRJ PROJECT
2 FCG FINAL COURSE GRADE PRJ * 1


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

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

e-mail: efendi.nasibov@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 4 52
Preparation before/after weekly lectures 13 4 52
Preparation for Final Exam 1 30 30
Preparing presentations 2 8 16
Preparing Individual Assignments 4 12 48
Final 1 2 2
Midterm 0
TOTAL WORKLOAD (hours) 200

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.15555
LO.25555
LO.35555
LO.45555
LO.55555

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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