Information Package / Course Catalogue - Adnan Menderes University

General Information Unit Staff Academic Information Programme Outcomes Course Structure Diagram Contact Information

Person Responsible for Information Package

Ins. Ümit BULUT

Data Mining

Course Code	: YZO207
Course Type	: Area Elective
Couse Group	: Short Cycle (Associate's Degree)
Education Language	: Turkish
Work Placement	: N/A

Theory	: 2
Prt.	: 0
Credit	: 2
Lab	: 0
ECTS	: 4

Objectives of the Course

Learning data mining techniques and concepts, analyzing the application area and selecting appropriate data mining techniques, designing and implementing data mining models, and interpreting model results.

Course Content

Data mining and knowledge discovery Data cleaning and preprocessing Classification methods Clustering methods Association rules Text mining Model Evaluation.

Name of Lecturer(s)

Learning Outcomes

1.	Mastering the basic data mining topics
2.	To be able to classify on a given data set
3.	Ability to perform clustering/bundling on a given data set
4.	Ability to analyze big data
5.	Ability to apply data mining algorithms to various data sets
6.	Ability to follow up on current problems and research topics related to data mining

Recommended or Required Reading

1.	Han, J., and Kamber M., 2006. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers. ISBN 1-55860-489-8.
2.	Pang-Ning Tan, Michael Steinbach, Vipin Kumar (2005). Introduction to Data Mining. Addison Wesley, ISBN: 0-321-32136-7

Weekly Detailed Course Contents

Week 1 - Theoretical

Definition of data mining

Week 2 - Theoretical

Overview of data mining application areas, techniques, and models

Week 3 - Theoretical

Creating a fit-for-purpose dataset

Week 4 - Theoretical

Data extraction and preprocessing

Week 5 - Theoretical

Data reduction and data transformation

Week 6 - Theoretical

Choosing a data mining learning algorithm

Week 7 - Theoretical

Model evaluation and information presentation, interpretation of the information found

Week 8 - Theoretical

Examining Data Mining learning algorithms: decision trees (midterm exam)

Week 9 - Theoretical

Classification, Curve fitting

Week 10 - Theoretical

Correlation, Memory-based methods,

Week 11 - Theoretical

k-neighbor algorithm

Week 12 - Theoretical

Bundling

Week 13 - Theoretical

R software and project examples

Week 14 - Theoretical

R software and project examples

Assessment Methods and Criteria

Type of Assessment	Count	Percent
Assignment	1	%10
Quiz	1	%10
Midterm Examination	1	%20
Final Examination	1	%60

Workload Calculation

Activities	Count	Preparation	Time	Total Work Load (hours)
Lecture - Theory	14	0	2	28
Assignment	1	16	1	17
Quiz	1	16	1	17
Midterm Examination	1	16	1	17
Final Examination	1	20	1	21
TOTAL WORKLOAD (hours)				100

Contribution of Learning Outcomes to Programme Outcomes

	PÇ-1	PÇ-2	PÇ-3	PÇ-4	PÇ-5	PÇ-6	PÇ-7	PÇ-8	PÇ-9	PÇ-10	PÇ-11	PÇ-12
OÇ-1	4	5	4	4	4	5	4	5	4	3	5	4
OÇ-2	5	5	5	4	5	4	5	4	5	3	4	5
OÇ-3	5	5	5	4	4	4	4	4	4	3	4	5
OÇ-4	5	5	5	4	4	4	5	4	4	3	5	4
OÇ-5	3	4	5	4	5	4	4	4	5	4	4	5
OÇ-6	5	5	5	4	4	4	3	4	5	4	5	4

Person Responsible for Information Package

Ins. Ümit BULUT