COURSE UNIT TITLE

: STATISTICAL METHODS IN DATA MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4138 STATISTICAL METHODS IN DATA MINING ELECTIVE 3 0 0 5

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR ALPER VAHAPLAR

Offered to

Statistics
Statistics(Evening)

Course Objective

This course aims to learn students the data mining concept, statistical methods used in data mining and to apply these methods to address different data mining goals and to real-world problems.

Learning Outcomes of the Course Unit

1   Describing the concepts of data mining and OLAP
2   Explaining supervised and unsupervised learning
3   Using data preparations
4   Using of clustering, classification and association rule algorithms
5   Comparing properties of algorithms
6   Using data mining softwares
7   Evaluating models and results
8   Developing data mining project

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Database, data warehouse and OLAP
2 Data mining process, CRISP-DM
3 Data mining process, data preparation
4 Unsupervised learning, clustering, hierarchical clustering
5 k-means, density based clustering
6 Supervised learning, classification methods
7 k-nearest neighbor method
8 Midterm exam
9 Decision tree algorithms, CART
10 Decision tree algorithms, C4.5
11 Neural networks
12 Association rules, Application of association rules
13 Model evaluation
14 Application of data mining, presentation of student projects

Recomended or Required Reading

Textbook(s):
Han, J. , Kamber, M., Pei, J., Data Mining: Concepts and Techniques. 3rd Ed., Morgan Kaufmann Publishers, 2011.
Supplementary Book(s):
1. Larose, Daniel T., Discovering Knowledge In Data An Introduction to Data Mining. New Jersey: John Wiley and Sons Ltd, 2005.
2. Alpaydın, E. , Introduction to Machine Learning. Second Ed. London:MIT Press, 2010.
3. Tan, P., Steinbach, M., Kumar, V., Introduction to Data Mining, Addison Wesley, 2006.
Materials: Lecture slides

Planned Learning Activities and Teaching Methods

Lecture, homework assignments, examples and PC laboratory exercises.

Assessment Methods

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


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

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams and homeworks.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

DEU Faculty of Sciences Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
Phone:+90 232 301 86 04

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparation before/after weekly lectures 12 1 12
Preparing assignments 1 31 31
Preparation for Final Exam 1 30 30
Preparing Group Assignments 0 0 0
Final 1 2 2
Project Assignment 1 2 2
TOTAL WORKLOAD (hours) 116

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.1555
LO.2555
LO.3555
LO.4555
LO.5555
LO.6555
LO.7554532
LO.8554334532

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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