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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
ISY 668DATA MINING3 + 03rd Semester10

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
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14570
Assignments14456
Mid-terms12020
Final examination12020
Presentation / Seminar Preparation21122
Report / Project31030
Total Work Load

ECTS Credit of the Course






260

10
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2022-2023 Spring1HÜSEYİN KOÇAK
Details 2021-2022 Fall1HÜSEYİN KOÇAK
Details 2020-2021 Fall1HÜSEYİN KOÇAK


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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
WeeksTopics
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 MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes