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

hypotheses.

Making judgements

Making judgements on the appropriate statistical models and methods to

be used for analyzing a specific dataset and on the interpretation of the

experimental results.

Communication skills

Communication skills in order to present a statistical analysis, including

both the methodology and the final conclusions, in a consistent and

convincing way.

Learning skills

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