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.
Miguel dos Santos Fonseca
Weekly - 3
Total - 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
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.
- 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.
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: