Propedeutic StudiesModule Computer science
Academic Year 2024/2025 - Teacher: ALFREDO PULVIRENTIExpected 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
Introduction to probability and statistics: • Introduction to probability; • Events; • Definition of probability; • Conditioned events; • Bayes Theorem; • Discrete Random Variables; • Expectation, Variance, Covariance, Standard Deviation; • Bernoulli distribution; Binomial Distribution; Hypergeometric Distirbution; Negative Bionomial Distribution; Geometric Distribution; Poisson Distribution; • Continuos Random Variables; • Uniform distribution; Exponoential Distribution; Gaussian Distribution; • Examples and excercices; • Introduction to Descriptive Statitiscs; • The data, variables, variability, indeces; • Statistical Inference: parameter estimation, statistical tests; •; • Examples and excercies; • Introduction to R.
Textbook Information
Lantieri PB, Risso D, Ravera G: Statistica medica per le professioni sanitarie, II ed. McGraw-Hill
Course Planning
Subjects | Text References | |
---|---|---|
1 | Introduzione alla bioinformatica: tipi di dati, problemi, strumenti. | Capitolo 1 + materiale didattico integrativo fornito dal docente |
2 | Sequenze, ricerca tramite BLAST, allineamento pairwise e multiplo. Algoritmi. | Capitoli 3 e 4 + materiale didattico integrativo fornito dal docente |
3 | Attività pratica su allineamento di sequenze | Capitolo 3 + Appendice A +materiale didattico integrativo fornito dal docente |
4 | Banche dati biologiche presenti sul sistema dell'NCBI: nucleotide, protein, OMIM, PUBMED, GENE, SNP | Capitolo 2 + materiale didattico integrativo fornito dal docente |
5 | Attività pratica su banche dati | Capitolo 2 + Appendice A + materiale fornito dal docente |
6 | Banca dati UNiPROT | Capitolo 7 +materiale didattico fornito dal docente |
7 | Cenni sulla programmazione R | https://cran.r-project.org/doc/contrib/manuale.0.3.pdf |
8 | Introduzione, statistica descrittiva | Capitoli 1 e 3 + materiale didattico integrativo |
9 | Raccolta e organizzazione dei dati, Indici di tendenza centrale e dispersione | Capitoli 4 5 e 6 + materiale didattico integrativo |
10 | calcolo delle probabilità e distribuzioni di probabilità | Capitolo 8 + materiale didattico integrativo |
11 | Rappresentazione grafica dei dati | Capitolo 7 + materiale didattico integrativo |
12 | Campionamento e inferenza statistica | Capitolo 10 + materiale didattico integrativo |
13 | Stime di parametri per intervallo | Capitolo 9 + materiale didattico integrativo |
14 | Test di ipotesi | Capitolo 10 + materiale didattico integrativo |