Biostatistics and Bioinformatics
A.Y. 2025/2026
Learning objectives
The objective of the course is to teach the student the basic knowledge of biostatistics and bioinformatics. Such knowledge will be fundamental for a proper experimental design and data analysis, as well as for statistical interpretation and evaluation of experimental results.
Expected learning outcomes
1. Knowledge and Understanding:
By the end of the course, the student should demonstrate knowledge of the fundamental principles of Biostatistics and Bioinformatics, the most common analytical methodologies, and the tools used to study and solve problems related to the statistical analysis of biological data and their interpretation. Additionally, the student should understand the theoretical and practical concepts that govern the management and statistical analysis processes of biological data related to animals in livestock production.
2. Ability to Apply Knowledge and Understanding:
The student should demonstrate the ability to apply knowledge and concepts to solve practical problems related to the application of statistical analyses of data concerning animal-origin food production. The student should show the ability to use specific technical and methodological tools (e.g., calculation software) to address case studies, analyze real data, and propose concrete solutions in the field of animal husbandry.
3. Critical Thinking and Judgment:
The student should demonstrate the ability to critically argue the information acquired by evaluating sources, data, and proposed models. Specific activities aimed at developing this ability include practical exercises on analyzing performance data from animals in livestock production, group work on complex case studies, and writing reports that require critical analysis and proposing well-founded interpretations.
4. Ability to Communicate What Has Been Learned:
The student should demonstrate the ability to express themselves using scientifically appropriate terminology, particularly concerning biostatistics. Exercises, oral presentations, and participation in group discussions are intended to stimulate the ability to communicate correctly, defend their ideas, and engage in scientific discussions with peers and instructors.
5. Lifelong learning skills:
The student should demonstrate the ability to use the knowledge acquired to interpret new phenomena and address complex issues. They should be able to utilize available knowledge sources (e.g., scientific databases, publications, learning platforms) and organize their own study autonomously, developing a critical and informed approach to emerging topics in the field.
By the end of the course, the student should demonstrate knowledge of the fundamental principles of Biostatistics and Bioinformatics, the most common analytical methodologies, and the tools used to study and solve problems related to the statistical analysis of biological data and their interpretation. Additionally, the student should understand the theoretical and practical concepts that govern the management and statistical analysis processes of biological data related to animals in livestock production.
2. Ability to Apply Knowledge and Understanding:
The student should demonstrate the ability to apply knowledge and concepts to solve practical problems related to the application of statistical analyses of data concerning animal-origin food production. The student should show the ability to use specific technical and methodological tools (e.g., calculation software) to address case studies, analyze real data, and propose concrete solutions in the field of animal husbandry.
3. Critical Thinking and Judgment:
The student should demonstrate the ability to critically argue the information acquired by evaluating sources, data, and proposed models. Specific activities aimed at developing this ability include practical exercises on analyzing performance data from animals in livestock production, group work on complex case studies, and writing reports that require critical analysis and proposing well-founded interpretations.
4. Ability to Communicate What Has Been Learned:
The student should demonstrate the ability to express themselves using scientifically appropriate terminology, particularly concerning biostatistics. Exercises, oral presentations, and participation in group discussions are intended to stimulate the ability to communicate correctly, defend their ideas, and engage in scientific discussions with peers and instructors.
5. Lifelong learning skills:
The student should demonstrate the ability to use the knowledge acquired to interpret new phenomena and address complex issues. They should be able to utilize available knowledge sources (e.g., scientific databases, publications, learning platforms) and organize their own study autonomously, developing a critical and informed approach to emerging topics in the field.
Lesson period: First 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
First semester
Course syllabus
Frontal teaching (32 hours):
- Data, variables, distributions, their presentation and synthesis (3 hours);
- Descriptive statistics - Position and variability indicators (4 hours);
- Hypothesis testing in statistics (3 hours);
- Inferential statistics - Frequency analysis, χ2 distribution and statistical test (3 hours);
- Analysis of biological data normal distribution, standardized normal distribution, use of normal distribution tables (3 hours);
- The difference between means of a sample and inference of results in the population, t-test (2 hours);
- Correlation and correlation absence test (1 hour);
- Solving systems of equations with matrix algebra - linear models (3 hours)
- Linear regression and hypothesis testing with ANOVA (4 hours);
- ANOVA through the use of linear models (6 hours);
Practical labs (32 hours):
Application of the topics of the course on real datasets through the use of Excel and RStudio.
- Data, variables, distributions, their presentation and synthesis (3 hours);
- Descriptive statistics - Position and variability indicators (4 hours);
- Hypothesis testing in statistics (3 hours);
- Inferential statistics - Frequency analysis, χ2 distribution and statistical test (3 hours);
- Analysis of biological data normal distribution, standardized normal distribution, use of normal distribution tables (3 hours);
- The difference between means of a sample and inference of results in the population, t-test (2 hours);
- Correlation and correlation absence test (1 hour);
- Solving systems of equations with matrix algebra - linear models (3 hours)
- Linear regression and hypothesis testing with ANOVA (4 hours);
- ANOVA through the use of linear models (6 hours);
Practical labs (32 hours):
Application of the topics of the course on real datasets through the use of Excel and RStudio.
Prerequisites for admission
No prerequisite
Teaching methods
The course is based on class frontal lectures and computer practice sessions. For the computer sessions, Microsoft Excel and R plus public domain software will be used. The software allows the management and statistical analysis of data useful for understanding the course topics.
Teaching Resources
-) Class-notes provided by the teacher
-) The R software for statistical computing" (https://www.r-project.org/) e Rstudio (https://www.rstudio.com/).
-) The R software for statistical computing" (https://www.r-project.org/) e Rstudio (https://www.rstudio.com/).
Assessment methods and Criteria
The exam will consist into 1 oral test.
Brief description of the test procedures:
The student will be asked to present a statistical analysis performed on a data set provided by the teacher. Specific topics of the course program will be also asked to the student during the oral exam
Brief description of the test procedures:
The student will be asked to present a statistical analysis performed on a data set provided by the teacher. Specific topics of the course program will be also asked to the student during the oral exam
SECS-S/01 - STATISTICS - University credits: 6
Practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professors:
Bagnato Alessandro, Bernini Francesca
Professor(s)