Statistics and Information Systems


To enable students to apply methods from statistics and computer science to real-world data sets in order to answer business and other questions, in particular with application to questions in long and short term insurance, social security, retirement benefits, healthcare and investment.

Practical problems corresponding to real situations will be privileged.

General characterization





Responsible teacher

Miguel dos Santos Fonseca


Weekly - 3

Total - Available soon

Teaching language



Available soon


Johnson, R. and Wichern, D. W. (2007), Applied Multivariate Statistical Analysis, 6th Edition, Prentice Hall, NewJersey.

Hastie, Trevor, Tibshirani, Robert, Friedman, Jerome (2009). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Second Edition. Springer-Verlag, New York

Bishop, Christopher (2006). Pattern Recognition and Machine Learning, Springer-Verlag, New York

Dalgaard, P. (2008), Introductory Statistics with R, Springer-Verlag, New York

Spector, P. (2008), Data manipulation with R, Springer-Verlag, New York

Teaching method

The main objective of the Curricular Unit is to provide students with computational skills suitable for the statistical treatment of large data sets.

The teaching methodologies will be essentially of a practical nature, in computer labs, after introducing the elementary concepts of adopted software.

Evaluation method

Continuous evaluation

  • Test (T)
  • Group Assignment (TG)

Final Grade = 0.6T + 0.4TG

Failure to complete any component implies a zero score, in this component, for calculating the Final Grade.

Grade defense from 17 values (inclusive) with additional assessment.

Subject matter

1 Data as a Resource For Problem Solving

1.1 Describe the possible aims of a data analysis (e.g. descriptive, inferential, predictive).

1.2 Describe the stages of conducting a data analysis to solve real-world problems in a scientific

manner and describe tools suitable for each stage.

2 Data Analysis

2.1 Describe the purpose of exploratory data analysis. 

2.2 Use a computer package to fit a generalized several statistical models..

3 Statistical Learning

3.1 Explain the meaning of the terms statistical learning and machine learning and the difference

between supervised learning and unsupervised learning.

4 Professional And Risk Management Issues

4.1 Explain the ethical and regulatory issues involved in working with personal data and

extremely large data sets.

5 Visualizing Data and Reporting

5.1 Create appropriate data visualizations to communicate the key conclusions of an analysis.


Programs where the course is taught: