1. FIRST CYCLE - BACHELOR'S DEGREE
  2. FACULTY OF ENGINEERING
  3. INDUSTRIAL ENGINEERING DEPARTMENT
  4. 255 Industrial Engineering
Course Information Course Learning Outcomes Course's Contribution To Program ECTS Workload Course Details
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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
CENG 468DATA MINING3 + 05th 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
1application ability about the data mining methods

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Assignments4624
Mid-terms11010
Final examination11414
Presentation / Seminar Preparation11414
Report / Project12626
Total Work Load

ECTS Credit of the Course





130

5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2022-2023 Spring1DUYGU TOPALOĞLU


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
CENG 468 DATA MINING 3 + 0 1 Turkish 2022-2023 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Lecturer DUYGU TOPALOĞLU dtopaloglu@pau.edu.tr MUH A0425 %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
2
3
4
5
6
7
8
9
10
11
12
13
14
Materials
Materials are not specified.
Resources
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam25Midterm Exam
Project25Project
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes