Data Analysis

Code:

2GE07

Acronym:

DA

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2021/2022 - 1S

Active?	Yes
Web Page:	n.a.
Responsible unit:	Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible:	Master in Management

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIM	12	Bologna Official Syllabus	2	-	7,5	56	202,5

Teaching language

English

Objectives

Graduate students in methods of data analysis, so that the students know to extract the essential information of a potentially large set of data.

Learning outcomes and competences

1. Understanding the theorectical foundations of the presented methods.

2. Capacity to extract essential information from a data set of real data, using the learned methodologies.

Working method

Presencial

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

The students should master the following topics:

Linear Algebra at 1st cycle level:
- Calculus with vectors and matrices, eigenvalues, eigenvectors

Probability and Statistics at 1st sycle level:
- Descriptive statistics;
- Basic notions of probability calculus;
- Probability distributions;
- Hypothesis testing.

Program

- Exploratory data analysis.

- Contingency tables.

- Analysis of Variance (ANOVA).

- Factorial analysis.

- Clustering analysis.

Mandatory literature

João Maroco; Análise Estatística com o SPSS Statistics, ReportNumber, 2011. ISBN: 9789899676329 (Previous editions frm the same author exist in the lirbary, with a similar title)
Everitt Brian S.; Applied multivariate data analysis. ISBN: 978-0-470-71117-0

Complementary Bibliography

Figueiredo Fernanda Otília de Sousa 070; Estatística descritiva e probabilidades. ISBN: 978-972-592-249-1
Figueiredo Fernanda Otília de Sousa 070; Inferência estatística. ISBN: 978-972-592-501-0
Hair Jr Joseph F; Multivariate data analysis. ISBN: 0-13-515309-3

Comments from the literature

n.a.

Teaching methods and learning activities

- Theoretical-practical classes, including resolution of problems and examples of software use.

- Exercises are proposed for individual practising.

- Follow-up of the practical assignement.

 

Software

Software R
SPSS
SPAD

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

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

Amount of time allocated to each course unit

Designation	Time (hours)
Apresentação/discussão de um trabalho científico	6,00
Estudo autónomo	117,00
Frequência das aulas	42,00
Trabalho escrito	37,50
Total:	202,50

Eligibility for exams

Final exam and practical assignement, both mandatory.

Calculation formula of final grade

- Final mark =  0.6 *exam's mark+0.4*practical assignment's mark.

- Approval is conditional to having a mark not bellow 7.0 (on a 0-20 scale) in the final exam.

Examinations or Special Assignments

The practical assignment consists in the analysis of a real data set, by the methods presented, and using statistical software.
It is supposed to be done by groups of three students.

Three deadlines corresponding to three phases :

- Definition of Groups and Topic (Data)
- Univariate analysis
- Multivariate analysis

Exact deadlines and more details to be specified in the first classes.

Internship work/project

n.a.

Special assessment (TE, DA, ...)

n.a.

Classification improvement

The mark may be improved in the 2nd exam. However, this possible improvement only has an effect in the exam's mark. There is no possibility to improve the mark obtained in the practical assignement.

The practical assignement is only valid for the current school year 2021/2022.

Observations

n.a.