COURSE UNIT TITLE

: INTRODUCTION TO DATA MINING

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BIL 3013 INTRODUCTION TO DATA MINING COMPULSORY 2 2 0 6

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR METE EMINAĞAOĞLU

Offered to

Computer Science

Course Objective

This course aims to teach the data mining concepts, make an analysis of the related system, and apply methods used in data mining to address different data mining goals and to real-world problems.

Learning Outcomes of the Course Unit

1   Have a good understanding of data mining concepts
2   Have ability to identify the current system and describe data used in this system
3   Be capable to prepare data for the specific methods
4   Have a good understanding and interpretation of methods used in data mining
5   Evaluate, develope and deploy the project to real life

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, basic concepts
2 Database, Data warehouses
3 Knowledge Discovery in Databases
4 Data understanding, Data visualization
5 Data preparation
6 Clustering methods, hierarchical clustering
7 k-means clustering, density based clustering
8 Midterm Exam
9 Classification methods, k-nearest neighbor algorithm
10 Decision trees, C4.5, CART
11 Neural Networks, fundamentals
12 Neural Networks - cont'd
13 Model evaluation
14 Data mining applications

Recomended or Required Reading

Textbook(s):
- Han, J. , Kamber, M., Pei, J., Data Mining: Concepts and Techniques. 3rd Ed., Morgan Kaufmann Publishers, 2011
- Larose, Daniel T., Discovering Knowledge In Data An Introduction to Data Mining. New Jersey: John Wiley and Sons Ltd, 2005
Supplementary Book(s): Tan, P., Steinbach, M., Kumar, V., Introduction to Data Mining, Addison Wesley, 2006

Planned Learning Activities and Teaching Methods

Course is taught in a lecture, class presentation.

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

1 Midterm exam, Assignments and a Final exam. (Exams will be software projects, won't be written exams)

Language of Instruction

Turkish

Course Policies and Rules

Students will come to the class in time. Attending the 70% of the classes are mandotary.

Contact Details for the Lecturer(s)

cagin.kandemir@deu.edu.tr

Office Hours

Will be announced.

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 12 3 36
Preparation for midterm exam 1 10 10
Preparation for final exam 1 20 20
Preparing assignments 2 8 16
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 138

Contribution of Learning Outcomes to Programme Outcomes

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

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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