Information Systems Engineering
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

FET308

Ders İsmi:

Data Mining

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Laboratory	ECTS
2	1	0	5

Language of instruction:

Turkish

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Bölüm Seçmeli

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 :

Asst. Prof. Dr. SAJJAD NEMATZADEH MİANDOAB

Course Lecturer(s):

Asst. Prof. Dr. SAJJAD NEMATZADEH MİANDOAB

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:	This course aims to explain the basic concepts of data mining to students in an applied way.
Course Content:	The course covers data science fundamentals, programming with python, data preparation, descriptive methods and predictive methods.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
2 - Skills
Cognitive - Practical
1) Students learn the importance of data science.
2) Students gain experience in Python programming.
3) Students understand the importance of data preparation.
4) Students gain the ability to comprehend descriptive methods.
5) Students gain the ability to comprehend predictive methods.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Fundamentals of Data Mining
2)	Data and Data Science Concepts
3)	Data Discovery
4)	Python Programming
5)	Data Editing with Python
6)	Predictive Methods
7)	Decision Trees
8)	Midterm
9)	Classification and Regression
10)	Machine Learning
11)	Descriptive Methods
12)	Clustering
13)	Association Rules
14)	Project Presentations
15)	Final Exam

Sources

Course Notes / Textbooks:	TAN, Pang-Ning; STEINBACH, Michael, KUMAR, Vipin, Introduction to data mining. Pearson Education India, 2016.
References:	AGGARWAL, Charu C. Data mining: the textbook. Springer, 2015.

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3	4	5
Program Outcomes

Ders - Öğrenme Kazanımı İlişkisi

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

Program Outcomes

Level of Contribution

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
total		%
PERCENTAGE OF SEMESTER WORK		% 0
PERCENTAGE OF FINAL WORK		%
total		%