Methodologies and Data Analysis


This course aims to:

- Allow the understanding of relevant information for the study of digital transformation and the impact of societal changes, at the individual and group level;
- Enable the identification of relevant information, its collection and processing in order to allow the application of appropriate methodologies, using statistical/econometric software, in order to obtain relevant results and, through their interpretation, respond to research challenges in test;
- Know the sources of information, statistical procedures to obtain new information and critically understand different statistical and econometric methods and possibly their extensions.

General characterization





Responsible teacher

Available soon


Weekly - Available soon

Total - 56

Teaching language



No requirements.


Agresti, A., Franklin, C. and Klingenberg, B., 2017, Statistics: The Art and Science of Learning from Data, Pearson
Anderson, T., 2003, An Introduction to Multivariate Statistical Analysis, Wiley-Interscience
Marsh C. and Elliot J., 2008, Exploring Data: An Introduction to Data Analysis for Social Scientists, Polity
Fielding J. and Gilbert N., 2006, Understanding Social Statistics, SAGE Publications
Johnson, R. and Wichern, D., 2008, Applied Multivariate Statistical Analysis, Pearson
Miles, M., Huberman, A. and Saldanża, J., 2019, Qualitative data analysis: A methods sourcebook, SAGE Publications
Moore, D., McCabe, G. and Craig, B.,2014, Introduction to the Practice of Statistics, W. H. Freeman
Reis, E., Melo, P., Andrade, R. e Calapez, T., 2021, Estatística Aplicada, Edições Sílabo
Stock, J. and Watson, M., 2019, Introduction to Econometrics, Pearson
Wooldridge, J., 2019, Introductory Econometrics: A Modern Approach, Cengage Learning

Teaching method

Theory-practice lecture.

Evaluation method

Evaluation is alternatively:
- Continuous, composed of three mini-projects, as group work, whose average score weighs 45% in the final grade, and the respective oral presentation, whose grade weighs 15% in the final grade; these elements constitute Frequency, by means of a score of not less than 9.5 points (out of 20); and an individual test, including a practical part, whose score weighs 40% of the final grade, with a minimum score of not less than 9.5 points being required;
- Individual final exam on the whole subject, whose score weighs 40%

Subject matter

1. Qualitative, quantitative information and measurement units
2. Visualization of data and descriptive statistics
3. Comparison between groups
4. Measures of association
5. Factor analysis and principal components
6. Multiple correspondence methods
7. Classification and forecasting - clusters, discriminant analysis and regression trees
8. Multiple regression with continuous variables and with discrete variables
9. Estimation, Inference and Forecasting
10. Topics and extensions, including time series, panel and spatial data and machine learning


Programs where the course is taught: