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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
YBS 441DATA MINING TECHNIQUES2 + 15th Semester5

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

COURSE'S CONTRIBUTION TO PROGRAM
PO 01PO 02PO 03PO 04PO 05PO 06PO 07PO 08PO 09PO 10PO 11PO 12
LO 001      544444
LO 002      323452
LO 003      553455
LO 004      333333
LO 005      343333
Sub Total      191816182017
Contribution000000443443

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14342
Assignments21020
Mid-terms11010
Final examination11616
Total Work Load

ECTS Credit of the Course






130

5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2020-2021 Fall2HAMİD YEŞİLYAYLA


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
YBS 441 DATA MINING TECHNIQUES 2 + 1 2 Turkish 2020-2021 Fall
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Lecturer HAMİD YEŞİLYAYLA hyesilyayla@pau.edu.tr İİBF BB108 %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
WeeksTopics
1 Introduction to Data Mining
2 Introduction to Data Mining
3 Data Preprocessing
4 Data Warehouses
5 Association Rules
6 Classification 1
7 Classification 1
8 Midterm
9 Time Series Analysis
10 Clustering 1
11 Clustering 2
12 Intrusion Detection
13 Text Mining
14 Web Mining
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam60Final Exam
Midterm Exam40Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes