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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS YBS 5012 DATA MINING ELECTIVE 3 0 0 6

Offered By

Management Information Systems

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR KAAN YARALIOĞLU

Offered to

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 Mid-Term exam 8 Data estimation methods (Neural network) 9 Data estimation methods (Reinforced learning) 10 Data clustering techniques 11 Text mining 12 Web mining, social networks 13 Data mining case studies 14 Final exam

Recomended or Required Reading

Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers. ISBN 1-55860-489-8., 2006 Handbook of Statistical Analysis and Data Mining Applications; Robert Nisbet, John ElderIV, Gary Miner;2009

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.20 + FIN* 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.20 + RST* 0.50 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Student assessment will depend on mid-term exam, in-term assignments, presentations and final exam.

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 12 3 36 Preparations before/after weekly lectures 12 4 48 Preparation for final exam 1 18 18 Preparation for midterm exam 1 18 18 Preparing presentations 1 20 20 Preparation for quiz etc. 1 18 18 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 4 4 5 3 5 3 3 3 4 3 5 LO.2 4 4 5 3 5 3 3 3 4 3 5 LO.3 5 4 4 4 4 5 5 3 5 5 5 LO.4 4 5 5 5 5 4 4 3 5 4 5