PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
YBS 441	DATA MINING TECHNIQUES	2 + 1	7th Semester	5

COURSE DESCRIPTION

Course Level	Bachelor's Degree
Course Type	Elective
Course Objective	The aim of the course is give information about Data Mining Concepts, Preparing the Data, Statistical Classification Method (Naïve Bayes), Clustering Methods(K-Means, Hierarchical), Decision Trees and Decision Rules, Association Rules.
Course Content	Introduction to Data Mining / Data Mining Concepts / Preparing the Data / Data Reduction / Statistical Classification Method (Naïve Bayes) / Clustering Methods (K-Means) / Clustering Methods (Hierarchical) Decision Trees and Decision Rules Association Rules / Artificial Neural Networks
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

1	To be able to define basic data mining concepts
2	Apply data preprocessing
3	Identify the appropriate data mining technique to solve a specific problem
4	Design a data mining model
5	Apply a data mining algorithm

COURSE'S CONTRIBUTION TO PROGRAM

Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	23	23
Final examination	1	23	23
Total Work Load ECTS Credit of the Course			130 5

COURSE DETAILS

Select Year

Course Details

Course Code	Course Title	L+P Hour	Course Code	Language Of Instruction	Course Semester
YBS 441	DATA MINING TECHNIQUES	2 + 1	1	Turkish	2022-2023 Fall

Course Coordinator	E-Mail	Phone Number	Course Location	Attendance
Lecturer HAMİD YEŞİLYAYLA	hyesilyayla@pau.edu.tr		İİBF B0214	%70

Goals		The aim of the course is give information about Data Mining Concepts, Preparing the Data, Statistical Classification Method (Naïve Bayes), Clustering Methods(K-Means, Hierarchical), Decision Trees and Decision Rules, Association Rules.
Content		Introduction to Data Mining / Data Mining Concepts / Preparing the Data / Data Reduction / Statistical Classification Method (Naïve Bayes) / Clustering Methods (K-Means) / Clustering Methods (Hierarchical) Decision Trees and Decision Rules Association Rules / Artificial Neural Networks

Topics

Materials

Materials are not specified.

Resources

Course Assessment

Assesment Methods	Percentage (%)	Assesment Methods Title
Final Exam	60	Final Exam
Midterm Exam	40	Midterm Exam

L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student