Biostatistics
A.Y. 2024/2025
Learning objectives
The course aims to introduce students to biostatistics, i.e. the application of statistical principles to questions and problems in genomics, biology or medicine.
Expected learning outcomes
At the end of this class , the students are expected to:
- know basic techniques and tools for the synthetic and graphical analysis of the information provided by clinical data sets
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
- know the basic models for the representation and the analysis of random phenomena, with particular focus on genomics problems, and their application
- be able to apply methods and tools of biostatistics and survival analysis
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
- know basic techniques and tools for the synthetic and graphical analysis of the information provided by clinical data sets
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
- know the basic models for the representation and the analysis of random phenomena, with particular focus on genomics problems, and their application
- be able to apply methods and tools of biostatistics and survival analysis
- apply the methods and techniques of biostatistics to real data sets by means of the use of appropriate statistical software.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
MAT/06 - PROBABILITY AND STATISTICS - University credits: 1
SECS-S/01 - STATISTICS - University credits: 5
SECS-S/01 - STATISTICS - University credits: 5
Practicals: 24 hours
Lectures: 36 hours
Lectures: 36 hours
Professor:
Cappozzo Andrea
Professor(s)