• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS YBS 3018 DATA MINING AND INFORMATION EXTRACTION COMPULSORY 3 0 0 4

Offered By

Management Information Systems

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR CAN AYDIN

Offered to

Management Information Systems

Course Objective

To introduce students to basic practices, concepts and techniques related to data mining, to have knowledge about how data will be collected and classified, and how data can be analyzed through collected data.

Learning Outcomes of the Course Unit

1   1 Simple and inexplicable, unpredictable and useful patterns or the removal of information from a large amount of data 2   2 To learn data collection, classification, storage algorithms 3   3 To be able to approach data mining as a process 4   4 Specialize in data mining software 5   5 Using data mining methods in problem solving

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 1. Introduction to data mining 2 2. Data Cleaning and Preparation Techniques 3 3. Data Warehouse 4 4. Statistical Methods in Data mining 5 5. Data Classification Methods 6 5. Data Classification Methods 7 6. Data Classification Methods (Neural Networks) 8 7. Data Prediction Techniques (Strengthened learning) 9 8. Data Clustering Techniques 10 9. Text Mining 11 10.Web Mining, Social Networks 12 11. Sectoral Data Mining Examples 13 13. Sectoral Data Mining Examples 14 13.Applications with data mining techniques 15 14.Applications with data mining techniques

Recomended or Required Reading

Veri Madenciliği Yöntemleri ve R Uygulamaları - Bülent Altunkaynak - Seçkin Yayıncılık Data Veri Madenciliği - Veri Analizi Prof. Dr. Haldun Akpınar- Papatya Yayıncılık

Planned Learning Activities and Teaching Methods

Events are in "Assessment Methods" and "Workload Calculation" section detaily.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 MTEG MIDTERM GRADE MTEG * 1 3 FIN FINAL EXAM 4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Students will undergo two exams, one mid-term and one final.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Dokuz Eylül Üniversitesi - I.I.B.F. Yönetim Bilişim Sistemleri Bölümü - Dokuzçeşmeler Kampüsü - Buca - IZMIR 35160 Tel: 0 232 301 07 62 e-mail: cigdem.tarhan@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 12 3 36 Preparations before/after weekly lectures 12 5 60 Final 1 1 1 Midterm 1 1 1 TOTAL WORKLOAD (hours) 98

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 LO.1 5 5 5 LO.2 5 5 5 LO.3 5 5 LO.4 5 5 5 LO.5 5 5