The general evaluation criteria are available at https://www.uniud.it/it/didattica/corsi/area-scientifica/scienze-matematiche-informatiche-multimediali-fisiche/laurea/informatica/studiare/criteri.pdf
provided. The material will be provided on the dedicated university web page (https://elearning.uniud.it/moodle/).
descriptive and inferential statistics, as basic tools for research and data
analysis. The students are expected to acquire the fundamental skills in
order to perform autonomously the analysis of real datasets, involving
the use of basic statistical procedures.
Knowledge and understanding
Knowledge and understanding of descriptive statistics and of how to summarize data, of the notion of uncertainty and of the basics in
probability theory and sampling theory, of the fundamental concepts of
inferential statistics, of the procedures for studying the relationships
between variables and of the basic elements of hypothesis testing.
Applying knowledge and understanding
Understanding of statistical methods as useful instruments for research in
data science and computer science applications and ability to use
descriptive and inferential statistics in order to summarize information, to
analyze and interpret relationships between variables and to test
Making judgements on the appropriate statistical models and methods to
be used for analyzing a specific dataset and on the interpretation of the
Communication skills in order to present a statistical analysis, including
both the methodology and the final conclusions, in a consistent and
Learning skills based on the prerequisites that are required for
understanding autonomously a statistical report and for applying more
advanced statistical techniques.
More information on the degree course are available on https://www.uniud.it/it/didattica/info-didattiche/regolamento-didattico-del-corso/l-internet-things-big-data-machine-learning/all-B2
1) Descriptive statistics: variables; frequency distributions; location and variability indicators; bivariate analysis.
2) Probability: basic concepts; random variables; probabilistic models; random vectors and convergence concepts; sampling theory.
3) Inference: point estimation; confidence intervals; hypothesis testing; correlation; simple linear regression models
1) Borra, S., Di Ciaccio, A. (2021). Statistica. IV ed., Mc Graw-Hill
2) Navidi, W. (2006). Probabilità e Statistica per l’ingegneria e le scienze. McGraw-Hill
3) Iacus, S.M. (2006). Statistica. McGraw-Hill
4) Iacus, S. M., Masarotto, G. (2013). Laboratorio di statistica con R. II ed., Mc Graw-Hill
5) Crivellari, F. (2006). Analisi statistica dei dati con R. Apogeo