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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS YBS 8030 DATA MINING COMPULSORY 2 0 0 4

Offered By

Management Information Systems Non-Thesis(Distance Learning)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR CAN AYDIN

Offered to

Management Information Systems Non-Thesis(Distance Learning)

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

The activities are detailed in the 'Assessment Methods' and 'Workload Calculation' sections.

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

Further Notes About Assessment Methods

None

Assessment Criteria

Students' performances are measured with a midterm exam and a final exam.

Language of Instruction

Turkish

Course Policies and Rules

The rules applied by the department apply.

Contact Details for the Lecturer(s)

Doç.Dr. Can AYDIN can.aydin@deu.edu.tr

Office Hours

It will 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 5 5 Preparation for final exam 1 5 5 Final 1 1 1 Midterm 1 1 1 TOTAL WORKLOAD (hours) 110

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