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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS VYA 5016 UNSUPERVISED LEARNING METHODS COMPULSORY 3 0 0 6

Offered By

DATA MANAGEMENT AND ANALYSIS

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR SERKAN ARAS

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

The objective of the course is to give the most common multi-variate statistical techniques and the basic concepts, assumptions and applications of these techniques for business with software packages.

Learning Outcomes of the Course Unit

1   1. To be able to present relationship between two data groups with canonical data analysis. 2   2. To be able to classify units into predetermined groups according to many characteristics using discriminant or logistic regression. 3   3. To be able to interpret multi-dimensional variable space in a smaller principle or factor space with the help of principal component analysis or factor analysis. 4   4. To be able to examine the assumptions of the multi-variate statistical techniques on different techniques. 5   5. To be able to investigate secret structures behind multi-dimensional space which is describe a fact with the help of factor analysis.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Multivariates graphics 2 Missing data, Detection of outliers, Testing of assumptions (normality, heteroscedasticity etc) 3 Principal Component Analysis 4 Factor Analysis 5 Univariate and Multivariate Regression Analysis 6 Canonical Correlation Analysis 7 Conjoint Analysis 8 Applications 9 Logistic Regression Analysis 10 Discriminant Analysis 11 ANOVA and MANCOVA 12 Clustering Analysis 13 Path Analysis 14 Multi-Dimensional Scaling

Recomended or Required Reading

1- Johnson R.A., Wichern D.W., Applied Multivariate Statistical Analysis, Prentice Hall, New Jersey 2- Alpar R., Uygulamalı Çok Değişkenli Istatistiksel Yöntemler, Detay Yayıncılık, Ankara.

Planned Learning Activities and Teaching Methods

1-Lecture Method 2-Implementation Method 3-Method of Discussion Analysis on Determined Cases

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 FCG FINAL COURSE GRADE 3 FCGR FINAL COURSE GRADE MTE * 0.40 + FCG* 0.60 4 RST RESIT 5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + RST* 0.60

Further Notes About Assessment Methods

None

Assessment Criteria

The weighted average of the midterm grade, the midterm work and the final grade must be 75 and above.

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 3 42 Preparations before/after weekly lectures 13 3 39 Preparation for midterm exam 1 20 20 Preparation for final exam 1 20 20 Preparing assignments 5 5 25 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 152

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 LO.1 1 LO.2 1 1 1 LO.3 1 1 1 1 LO.4 1 LO.5 1 1 1 1