INFORMATICA E STATISTICA MEDICA
Module INFORMATICA

Academic Year 2025/2026 - Teacher: ALFREDO PULVIRENTI

Expected Learning Outcomes

General summary:

The course aims to acquire the main basic concepts of probability and statistics.

General teaching training objectives in terms of learning outcomes:

Knowledge and understanding: The course aims to acquire skills to students about the description of statistical data; Understand the basic terms (population, sample, variable, etc.); Calculation and presentation of frequency distributions; data description with graphical methods; Calculation of central tendency and variability indices; Understand the basis of the assessment of probability of an event and of a random variable; Acquiring concepts related to inferential statistics such as estimates for intervall confidence and hypothesis tests.
Applying knowledge and understanding: identify distributions appropriate to represent the knowledge underlying; solving problems of inferential statistics and probability.
Making judgments : Through concrete examples and case studies, the student will be able to independently develop solutions to specific problems and assess the suitability of a statistical inference problem and solution.
Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical language within the general framework of the analysis of data using statistical methods.
Learning skills: The course aims, as the goal, to provide students with the necessary theoretical and practical methods to address and solve problems independently in the statistical analysis of data.

Course Structure

Frontal Lectures.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
 

Learning assessment may also be carried out on line, should the conditions require it.

Detailed Course Content

Course Syllabus

  • Data representation
    Data types, information encoding, numeric and textual representations.

  • Spreadsheets
    Structure of a spreadsheet, basic formulas and functions, relative and absolute references, simple data analysis and visualization.

  • Introduction to relational databases

    • The relational model

    • Relational algebra (selection, projection, join, basic operations)

    • Conceptual database design (schemas, entities, relationships)

    • Overview of SQL (data definition and querying)

  • Introduction to programming

    • The notion of algorithm

    • Representation using flowcharts

    • Structured linear notation (pseudocode)

    • Introduction to programming in R (working environment, basic data types, simple scripts)

Textbook Information