Data Analysis

Code:

2MADSAD05

Acronym:

AD

Keywords
Classification	Keyword
OFICIAL	Statistics

Instance: 2015/2016 - 2S

Active?	Yes
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible:	Master in Modeling, Data Analysis and Decision Support Systems

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MADSAD	39	Bologna Official Syllabus	1	-	7,5	56	202,5
ME	3	Bologna Syllabus	1	-	7,5	56	202,5

Teaching language

English

Objectives

To form the students in methods for univariate, bivariate and multivariate analysis of data.

Learning outcomes and competences

- Understanding the theorectical foundations of the methods taught.

- Capacity of analysing a multivariate real data set, using the methods taught.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

The students are suppposed to master, at undergraduate level :

- Linear algebra : matrix calculus, matrix differentiation ; determination of eigenvalues and eigenvectors.

- Probability and Statistics :descritive statistics;notions of probabilty calculus;  multinormal distribution ;hypothesis testing.

Program

• Exploratory (preliminar) data analysis

• Contingency tables.

• Factorial Analysis : Principal Component Analysis; Simple Correspondence Analysis ;  Multiple Correspondence Analysis.

• Cluster Analysis : Comparison measures ; Hierarchical Clustering;Non-Hierarchical Clustering.

• Discriminant Analysis: Discriminant Analysis in 2 groups;Discriminant Analysis in K groups.

 

Mandatory literature

Subhash Sharma; Applied Multivariate Techniques, Wiley, 1996
João Maroco; Análise Estatística com utilização do SPSS, Edições Sílabo
L. Lebart, A. Morineau, M. Piron; Statistique Exploratoire Multidimensionnelle, Dunod, Paris,, 1995
H. Fenneteau, C. Biales ; Analyse Statistique des Données. Applications et cas pour le Marketing, Ellipses, Paris, 1993
Elisabeth Reis; Estatística Multivariada Aplicada, Edições Sílabo
B. Murteira, C. Silva Ribeiro, J. Andrade e Silva, C. Pimenta; Introdução à Estatística, McGraw-Hill

Complementary Bibliography

Diday, Lemaire, Pouget, Testu ; Éléments d'Analyse des Données, Dunod, Paris, 1982
B. S. Everitt; The Analysis of Contingency Tables, Chapman & Hall

Teaching methods and learning activities

- Theorectical-pratical classes, with examples of software using. 

- Exercises proposed for personal training.

- Follow-up of the practical assignement.

 

Software

SPAD
R
SPSS

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	60,00
Trabalho de campo	40,00
Total:	100,00

Eligibility for exams

Final exam and practical assignement.

Calculation formula of final grade

Final mark = 0.6* exam mark + 0.4*practical assignement mark.

Approving is conditioned to having a minimum mark of 7.0 in the final exam.

 

Examinations or Special Assignments

The practical assignement consists in analysing a real data set, using the methods taugh and one or more software packages.
It consists in two parts : 1st part : description of the data set, univariate and bivariate data analysis ; 2nd part : multivariate data analysis.

It should be made by groups of 2 (two) students.

The practical assignement has an oral presentation.