COURSE UNIT TITLE

: MULTIVARIATE DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 2202 MULTIVARIATE DATA ANALYSIS COMPULSORY 2 2 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR ESIN FIRUZAN

Offered to

Computer Science

Course Objective

Multivariate data occur in all branches of science. Teaching to students the multivariate statistical methods which are often met in real life is the objective of this course.

Learning Outcomes of the Course Unit

1   Understanding statistical concepts of linear algebra terms (rank, determinant, eigenvalu, eigenvector etc.),
2   Obtaining multivariate descriptive statistics (mean vector, variance-covariance matrix, correlation matrix etc.),
3   Interpreting three or more dimensional graphs,
4   Applying Principal Component Analysis,
5   Applying Factor Analysis,
6   Applying Discriminant Analysis for two multivariate normal populations.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Types of variables, Data Matrices and Vectors, Data Subscripts
2 The Multivariate Normal Probability Density Function, Bivariate Normal Distributions
3 Mean Vectors and Variance-Covariance, Correlation and Standardized data matrices
4 Three-Dimensional Data Plots, Plots of Higher Dimensional Data
5 Multivariate Normal DIstribution Contour Plot
6 Eigenvalues and eigenvectors, Geometric Descriptions
7 Principal Components Analysis (PCA)
8 Midterm Exam
9 Principal Components Analysis on the Variance-Covariance Matrix sigma
10 Estimation of Principal Components, PCA on the Correlation Matrix P
11 Objectives of Factor Analysis, Factor Analysis Equations
12 Choosing the Appropriate Number of Factors, Rotating Factors
13 Discriminant for two Multivariate Normal Populations
14 Cost Functions and Prior Probabilities, A General Discriminant Rule (Two Populations)

Recomended or Required Reading

Textbook(s):
Anderson T. W., An Introduction To Multivariate Statistical Analysis, Wiley-Interscience, 2003.
Alpar, R., Uygulamalı Çok Değişkenli Istatistiksel Yöntemler, Detay Yayıncılık, 2011
Supplementary Book(s):
Grinn, L. G. and Fidell, L. S., Reading and Understanding More Multivariate Statistics, APA Books, Washington D. C., 2000.
Tabachnick, B. G., & Fidell, L. S., Using Multivariate Statistics, Harper Collins College Publishers, 2001

Planned Learning Activities and Teaching Methods

Lecture, project and presentation

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 + ASG * 0.10 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.10 + RST * 0.50


Further Notes About Assessment Methods

If needed, other assessment methods can be added to the table given below.

Assessment Criteria

Optional, if the instructor needs to add some explanation or further note, this column can be selected from the DEBIS menu.

Language of Instruction

Turkish

Course Policies and Rules

Student responsibilities
Students will come to the class in time. Attending the 70% of the classes are mandotary.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 4 52
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 15 15
Preparation for final exam 1 18 18
Preparing presentations 1 8 8
Design Project 1 5 5
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 114

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.143
LO.24433
LO.34433
LO.444334
LO.544334
LO.644334

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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