• 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 8020 DATA MINING COMPULSORY 2 0 0 6

Offered By

Distance Learning Non-thesis Master's Degree in Management Information Systems

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR VAHAP TECIM

Offered to

Distance Learning Non-thesis Master's Degree in Management Information Systems

Course Objective

The course will introduce students to basic applications of data mining, concepts, and techniques and provide background on data acquisition, classification, analysis methods.

Learning Outcomes of the Course Unit

1   To be able to discriminate among methods for extracting unknown patterns or information from big data 2   To be able to list methods for data acquisiton, classification, and storage algorithms 3   To be able to show ability for approaching data mining a process model 4   To be able to experiment on selected data mining software

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to data mining 2 Data cleansing and preparation techniques 3 Data warehouse 4 Statistical methods in data mining 5 Data classification methods (Inductive learning, decision trees) 6 Data classification techniques (Relational rules, Bayes Theory) 7 Data estimation methods (Neural network) 8 Mid-Term exam 9 Data estimation methods (Reinforced learning) 10 Data clustering techniques 11 Text mining 12 Web mining, social networks 13 Data mining case studies 14 Application 15 Application

Recomended or Required Reading

To be announced.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FCG FINAL COURSE GRADE 3 FCG FINAL COURSE GRADE MTE * 0.20 + FIN* 0.80 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + RST* 0.80 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 2 28 Preparations before/after weekly lectures 14 5 70 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Preparing assignments 1 20 20 Final 1 2 2 Midterm 1 2 2 TOTAL WORKLOAD (hours) 162

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 LO.1 5 5 5 5 5 5 5 5 5 5 5 LO.2 5 5 5 5 5 5 5 5 5 5 5 LO.3 5 5 5 5 5 5 5 5 5 5 5 LO.4 5 5 5 5 5 5 5 5 5 5 5