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THIRD CYCLE - DOCTORATE DEGREE
THE GRADUATE SCHOOL OF SOCIAL SCIENCES
BUSINESS MANAGEMENT DEPARTMENT
2741 BUSINESS ADMINISTRATION PhD
Course Information
Course Learning Outcomes
Course's Contribution To Program
ECTS Workload
Course Details
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COURSE INFORMATION
Course Code
Course Title
L+P Hour
Semester
ECTS
ISY 668
DATA MINING
3 + 0
3rd Semester
10
COURSE DESCRIPTION
Course Level
Doctorate Degree
Course Type
Elective
Course Objective
To introduce students the basic knowledge, concepts and techniques of data mining. Prepare students for research on data mining
Course Content
Introduction to Data Mining, Steps of Data Mining, , Data Mining Methods (Classification, Clustering and Association / Association Rules),, Data Mining Applications
Prerequisites
No the prerequisite of lesson.
Corequisite
No the corequisite of lesson.
Mode of Delivery
Face to Face
COURSE LEARNING OUTCOMES
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
5
70
Assignments
14
4
56
Mid-terms
1
20
20
Final examination
1
20
20
Presentation / Seminar Preparation
2
11
22
Report / Project
3
10
30
Total Work Load
ECTS Credit of the Course
260
10
COURSE DETAILS
Select Year
All Years
2022-2023 Spring
2021-2022 Fall
2020-2021 Spring
2020-2021 Fall
Course Term
No
Instructors
Details
2022-2023 Spring
1
HÜSEYİN KOÇAK
Details
2021-2022 Fall
1
HÜSEYİN KOÇAK
Details
2020-2021 Fall
1
HÜSEYİN KOÇAK
Print
Course Details
Course Code
Course Title
L+P Hour
Course Code
Language Of Instruction
Course Semester
ISY 668
DATA MINING
3 + 0
1
Turkish
2022-2023 Spring
Course Coordinator
E-Mail
Phone Number
Course Location
Attendance
Assoc. Prof. Dr. HÜSEYİN KOÇAK
hkocak@pau.edu.tr
İİBF A0139
%80
Goals
To introduce students the basic knowledge, concepts and techniques of data mining. Prepare students for research on data mining
Content
Introduction to Data Mining, Steps of Data Mining, , Data Mining Methods (Classification, Clustering and Association / Association Rules),, Data Mining Applications
Topics
Weeks
Topics
1
Giriş
2
Microsoft Azure ML Studio
3
Data Manipulation
4
Prediction Methods
5
Prediction Methods
6
Prediction Methods
7
Prediction Methods
8
Case Studies
9
Clustering Analysis
10
Clustering Analysis
11
Clustering Analysis
12
Case Studies
13
Case Studies
14
Case Studies
Materials
Materials are not specified.
Resources
Course Assessment
Assesment Methods
Percentage (%)
Assesment Methods Title
Final Exam
50
Final Exam
Midterm Exam
50
Midterm Exam
L+P:
Lecture and Practice
PQ:
Program Learning Outcomes
LO:
Course Learning Outcomes
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Home Page
About University
Name And Address
Acedemic Authorities
General Discription
Academic Calendar
General Admission Requirements
Recognition of Prior Learning
General Registration Procedures
ECTS Credit Allocation
Academic Guidance
Information For Students
Cost Of Living
Accommodation
Meals
Medical Facilities
Facilities for Special Needs Students
Insurance
Financial Support for Students
Student Affairs
Learning Facilities
International Programs
Language Courses
Internships
Sports Facilities and Leisure Activities
Student Associations
Practical Information for Mobile Students
Degree Programmes