EDUCATIONAL TECHNOLOGY (ENGLISH, THESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP5102	Data Mining II	Fall	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi TEVFİK AYTEKİN
Course Lecturer(s):	Dr. Öğr. Üyesi TEVFİK AYTEKİN
Recommended Optional Program Components:	none.
Course Objectives:	This course introduces some advanced and popular data mining topics with practical implementations. Applications shall be made on the R open-source software program. Basic information will be taught to use R programming language.

Learning Outcomes

The students who have succeeded in this course;
I. To introduce advanced topics in data mining.
II. Data mining methods and advanced programming tools that are used in various engineering fields and to use their skills to practice.
III. And gain the ability to explore data relationships.
IV. For problems involving data provide the ability to perform hypothesis testing.

Course Content

Introduction, Data import and export, Data exploration, Decision trees and random forest, Network estimation, Outlier detection, Time serious analysis, Association rules, Text mining, Social network analysis, web mining, Case study I: Analysis and forecasting of house price indices, Case study II: Predictive modelling of Big Data with limited memory

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction
2)	Data import and export
3)	Data exploration
4)	Decision trees and random forest
5)	Network estimation
6)	Outlier detection
7)	Time serious analysis
8)	Association rules
9)	Text mining
10)	Social network analysis
11)	web mining
12)	Case study I: Analysis and forecasting of house price indices
13)	Vaka çalışması II: sınırlı bellek ile Büyük Veri Akıllı modelleme
14)	Projects

Sources

Course Notes / Textbooks:	Yanchang Zhao ,R and Data Mining: Examples and Case Studies, Academic Press, Elsevier, 2012
References:	none.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Workload
Course Hours	14	42
Study Hours Out of Class	14	42
Project	1	30
Midterms	1	30
Final	1	50
Total Workload		194

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
2)	To be able to comprehend the interdisciplinary interaction with which the field is related.
3)	To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
4)	To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines.
5)	To be able to solve the problems encountered in the field by using research methods.
6)	To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
7)	To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary.
8)	To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
9)	To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
10)	To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes.
11)	To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
12)	Being able to independently carry out a work that requires expertise in the field.
13)	To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
14)	Being able to lead in environments that require the resolution of problems related to the field.