Türkçe | English
FACULTY of ECONOMICS and ADMINISTRATIVE SCIENCES / DEPARTMENT of MANAGEMENT INFORMATION SYSTEMS

Course Catalog
Web: http://www.ktu.edu.tr/ybs
Phone: +90 0462 0462 377 29 64
İİBF
FACULTY of ECONOMICS and ADMINISTRATIVE SCIENCES / DEPARTMENT of MANAGEMENT INFORMATION SYSTEMS /
Katalog Ana Sayfa
  Katalog Ana Sayfa  KTÜ Ana Sayfa   Katalog Ana Sayfa
 
 
YBS3004Business intelligence and data mining3+0+0ECTS:5
Year / SemesterSpring Semester
Level of CourseFirst Cycle
Status Compulsory
DepartmentDEPARTMENT of MANAGEMENT INFORMATION SYSTEMS
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
LecturerDr. Öğr. Üyesi Fatih GÜRCAN
Co-Lecturer
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
In this course, it is aimed to gain knowledge and skills related to data mining concepts and techniques used in the process of knowledge discovery.
 
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : Identify data mining functionalities 1,3,5,8
LO - 2 : Identify data warehousing functionalities 1,3,5,8
LO - 3 : Apply data preprocessing techniques1,3,5,8
LO - 4 : Describe data mining primitives, languages, and system architectures 1,3,5,8
LO - 5 : Applies appropriate data mining techniques in large databases1,3,5,8
LO - 6 : Use data mining software to perform data mining functionalities 1,3,5,8
LO - 7 : Describe the current needs in data mining research1,3,5,8
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

 
Contents of the Course
Fundamentals of data mining, data mining techniques, data mining software and R
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Introduction to Data Mining
 Week 2Getting to know your data
 Week 3Data Preprocessing
 Week 4Data Warehousing and Online Analytical Processing
 Week 5Association rules
 Week 6Classification: Basic Concepts
 Week 7Classification: Advanced Methods
 Week 8Cluster Analysis
 Week 9Midterm exam
 Week 10Outlier detection
 Week 11Data mining trends and research frontiers
 Week 12Data Mining Applications
 Week 13Basics of Pandas
 Week 14Python and Data Mining Applications-I
 Week 15Python and Data Mining Applications-II
 Week 16Final exam
 
Textbook / Material
1Akpınar, Haldun. 2014. DATA Veri Madenciliği Veri Analizi (2. Baskı), Papatya Bilim
 
Recommended Reading
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 04/2020 1 50
End-of-term exam 16 06/2020 1 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 4 14 56
Arasınav için hazırlık 2 8 16
Arasınav 1 1 1
Uygulama 1 14 14
Dönem sonu sınavı için hazırlık 2 6 12
Dönem sonu sınavı 1 1 1
Total work load142