Academic Year 2023/2024 - Teacher: VALENTINA DI SALVATORE

Expected Learning Outcomes

At the end of the course, the student will gain familiarity with the fundamental concepts of mathematics, the basic principles of descriptive statistics, and computer science. They will possess a comprehensive knowledge of programming systems and the reasoning process. They will also understand the fundamental concepts of databases and computer networks and will be able to identify the main concepts associated with them. Finally, they will have a global vision of the mathematical, statistical, and computer applications to biomedical and pharmaceutical sciences.

Course Structure

Through lessons and practical sessions at the end of each learning unit (when planned).

If the lessons are given in a mixed or remote way, the necessary changes with respect to what was previously stated may be introduced, in order to meet the program envisaged and reported in the syllabus.

To guarantee equal opportunities and in compliance with the laws in force, interested students can ask for a personal interview in order to plan any compensatory and / or dispensatory measures, based on the didactic objectives and specific needs.

It is also possible to contact the referent teacher CInAP (Center for Active and Participated Integration - Services for Disabilities and / or SLD) of our Department, Prof. Teresa Musumeci

Required Prerequisites

There are no background knowledge required

Attendance of Lessons

Ruled by

Detailed Course Content


Elementary functions: power functions and n-th roots, exponential functions, and logarithmic functions: definitions, properties, graphs, applications. The use of exponentials and logarithms in life sciences: models for the evolution of a population, such as that of the bacteria in a culture or the cells in a tissue of an organism. Functions of a real variable: notes on the domain of definition, increase, decrease, maximum and minimum (absolute), composition of elementary functions and their graph. Limits: definitions, properties, calculation rules, order of infinity and infinitesimal, graphical aspects, oblique asymptotes. Derivatives. Integrals: definition, properties, area calculation, approximation using the trapezoid method. Using AI tools for mathematics.


Notes and principles of descriptive statistics. Multivariate analysis. Bivariate analysis. Biostatistics. Frequency measures. Risk measures. Distributions (Normal, Gaussian). Central limit theorem. Confidence intervals. Hypothesis testing. Significance.

Computer Science

Fundamental concepts of Information Theory; General concepts: Hardware, Software; Information Technology; Types of computers; Main components of a PC; Computer performance. Hardware: Central Processing Unit; Memory; Input peripherals; Output peripherals; Input/output peripherals; Memory devices. Software: Types of software; System software; Application software; Graphical User Interface; System development. Data mining. Data computerization. Systems for database management. Introduction to computer networks.


Examples of mathematical/statistical/computer applications in the field of Life Sciences and Drug Discovery.

Textbook Information

Teacher's notes

Learning Assessment

Learning Assessment Procedures

By written test and oral test.

Verification of learning can also be carried out electronically, should the conditions require it.