Computer Engineering (English)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General course introduction information

Course Code:

CENG394

Course Name:

Data Mining

Course Semester:

Spring

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	7

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Department Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Prof. Dr. PINAR YILDIRIM

Course Lecturer(s):

Course Assistants:

Course Objective and Content

Course Objectives:	The purpose of the course is to educate students about the main concepts and methods of data mining.
Course Content:	The course contains these topics: classification, clustering, association algorithms and data mining studies in different areas.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Ability to explain the concepts of data mining
2) Ability to use the algorithms of data mining.
2 - Skills
Cognitive - Practical
1) Ability to evaluate the outputs of data mining studies.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Lesson Plan

Week	Subject	Related Preparation
1)	Introduction to data mining
2)	Introduction to data
3)	Data preprocessing
4)	Classification
5)	Decision trees
6)	Classification evaluation and introduction to Weka software
7)	Instance based classification k-nn algorithm
8)	Artificial neural networks
9)	Clustering
10)	Midterm
11)	Statistical Classifier Naive Bayes Classifier
12)	Association rule mining
13)	Ensemble Learning
14)	Data science with NumPy and Pandas
15)	Final exam

Sources

Course Notes / Textbooks:	Data Mining Concept and Techniques J.Han and M.Kamber @2012\| Morgan Kaufmann Publishers ISBN 978-0-12-381479-1
References:	Yok / None

Course-Program Learning Outcome Relationship

Learning Outcomes	1	3	2
Program Outcomes
1) Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4) Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
6) The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7) Effective communication skills in oral and written communication; at least one foreign language knowledge.
8) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9) Professional and ethical responsibility.
10) Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
11) Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.

Course - Learning Outcome Relationship

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

	Program Outcomes	Level of Contribution
1)	Sufficient knowledge in mathematics, science and engineering related to their branches; the ability to apply theoretical and practical knowledge in these areas to model and solve engineering problems.
2)	The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modeling methods for this purpose.
3)	The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose. (Realistic constraints and conditions include such issues as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, according to the nature of design.)
4)	Ability to develop, select and use modern techniques and tools necessary for engineering applications; ability to use information technologies effectively.
5)	Ability to design experiments, conduct experiments, collect data, analyze and interpret results for examination of engineering problems.
6)	The ability to work effectively in disciplinary and multidisciplinary teams; individual work skill.
7)	Effective communication skills in oral and written communication; at least one foreign language knowledge.
8)	Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
9)	Professional and ethical responsibility.
10)	Information on project management and practices in business life such as risk management and change management; awareness about entrepreneurship, innovation and sustainable development.
11)	Information on the effects of engineering applications on health, environment and safety in the universal and social dimensions and the problems of the times; awareness of the legal consequences of engineering solutions.

Learning Activity and Teaching Methods

Lesson
Project preparation

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Midterms	1	% 50
Final	1	% 50
total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Workload
Course Hours	16	48
Midterms	12	90
Final	14	72
Total Workload		210