Data Analysis

Code:

2EAE14

Acronym:

AED

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2013/2014 - 1S (of 09-09-2013 to 20-12-2013)

Active?	Yes
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible:	Master in Economics and Business Administration

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
EAE	49	Bologna Syllabus since 2012	2	-	3,5	27	94,5

Teaching language

Suitable for English-speaking students

Objectives

 

Provide students with the knowledge and practical tools necessary for the design, development and exploitation of statistical projects, namely regarding the collection, processing and analysis of data.

 

Learning outcomes and competences

 

It is intended that students acquire autonomy in choosing ability of current techniques involving methods of data collection around the Web 2.0, spatial and spatial-temporal data analysis and processing of data with key measures and statistical techniques with applications to economics, management, Marketing, and other areas of knowledge.

 

Working method

Presencial

Program

• Presentation, Statistical Process; Main sampling techniques, sample size calculation, preparation of questionnaires; Data collection under web 2.0; New data collection techniques; Data types;
• Introduction to Data Analysis; measures of location, dispersion and skewness, outliers, and normality assumptions; box and whiskers, Introduction to SPSS; coding and recoding of data; creating and importing databases; crosstabs of qualitative data, frequency tables, graphs; descriptive statistics; Creating variables; exercises with SPSS
• Hypothesis testing; parametric and nonparametric tests; Analysis of Variance
• Multivariate Analysis: Introduction to Regression Analysis, Cluster Analysis, Factor Analysis

Mandatory literature

Maroco, João; Análise estatística. ISBN: 978-972-618-452-2

Teaching methods and learning activities

• Presentation, Statistical Process; Main sampling techniques, sample size calculation, preparation of questionnaires; Data collection under web 2.0; New data collection techniques; Data types;
• Introduction to Data Analysis; measures of location, dispersion and skewness, outliers, and normality assumptions; box and whiskers, Introduction to SPSS; coding and recoding of data; creating and importing databases; crosstabs of qualitative data, frequency tables, graphs; descriptive statistics; Creating variables; exercises with SPSS
• Hypothesis testing; parametric and nonparametric tests; Analysis of Variance
• Multivariate Analysis: Introduction to Regression Analysis, Cluster Analysis, Factor Analysis

Evaluation Type

Distributed evaluation with final exam

Assessment Components

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

Calculation formula of final grade

 

The evaluation will be done through an examination (weighting 60%) and practical work (weighting 40%). The minimum score of the exam is 7.5 points.