Introduction to statistics: descriptive statistics; inferential statistics; terminology; qualitative nominal and ordinal characters; discrete and continuous quantitative characters. (lesson 1)
Data representation: representation by statistical unit-modality; mode-frequency representation; absolute, relative, cumulative absolute, cumulative relative frequencies; relative frequency distribution; graphical representation of data; group data into classes. (lessons 2-3)
Indicators for the description of the distributions: central trend indices: arithmetic average, weighted average, average for grouped data, geometric average, mode; position indicators: median, median for pooled data, quartiles and percentiles; variability indicators: range, deviance, variance, standard deviation, standard deviation for pooled data, coefficient of variation, interquartile deviation; 5-number summary and boxplot. (lessons 4-7)
Distributions: distributions of observations; symmetric and asymmetric distributions; shape indices of a distribution: asymmetry (skewness) and kurtosis (kurtosis); Gaussian distributions N (µ, σ) and standard Gaussian N (0,1); standardization; find the proportion given an interval and find the interval given a proportion; Student's T distributions at different degrees of freedom. (lessons 8 - 11)
Samples and inference: sample mean, variance and standard deviation; inference and errors in the inference process; distribution of the sample mean and central limit theorem; standard error of the sample mean; confidence levels; confidence intervals for the mean; sampling techniques: fraction sampling, simple random sampling, systematic sampling, stratified sampling, quota sampling and cluster sampling; generation of random numbers with uniform distribution or with Gaussian distribution of mean and standard deviation fixed by MS Excel functions. (lessons 12-14)
Correlation, interpolation and regression: scatter plot; covariance; linear correlation coefficient; interpolating curves, residuals and least squares curves; line of least squares; coefficient of determination. (lessons 15-17)
Statistical tests for hypothesis testing: definition of probability and its interpretation as a limit of relative frequency; statistical tests; hypothesis H0 and H1; p-value and level of significance; 1st and 2nd type errors; power of the test; decision rule; operational sequence for conducting a statistical test; parametric and non-parametric tests; summary scheme of the types of tests; z-test and t-test for checking whether an observation belongs to a population. (lessons 18-19)
Test for the verification of the association between two characters: test for the verification of the association between quantitative characters based on the linear correlation coefficient of Bravais-Pearson; test for the verification of association between characters on an ordinal scale based on the Spearman rank correlation coefficient; examples using MS Excel. (lessons 20-21)
Chi-square test for frequency analysis: observed frequencies and expected frequencies; verification of frequency homogeneity, verification of goodness of adaptation of the empirical distribution to the expected trend of the theoretical distribution, verification of the association between two characters; correction of Yates in the case of only one degree of freedom; Fisher's exact test; McNemar test for paired data (before and after treatment); I use Excel functions. (lessons 22-26)
Test for the comparison of medians: transformation of observations into ranks; test for the comparison of the medians of unpaired observations (U of Mann - Whitney, K of Kruskal-Wallis); test for the comparison of the medians of paired observations (Wilcoxon's T); examples using MS Excel. (video 27-29)
Test for comparison of the averages: t-test for comparison of averages of unpaired and paired observations; examples using MS Excel. (lesson 30)
Elements of epidemiology: definitions, objectives and study models; recall of mathematical tools: ratios, proportions, rates, odds; disease frequency measures: prevalence, cumulative incidence and incidence rate, odds; static cohort and dynamic cohort; measures of association: relative risk (RR) and odds ratio (OR), interpretative scale, confidence intervals; prospective studies (or cohort studies or longitudinal studies): epidemiological studies and clinical trials, single blind and double blind, hazard ratio; retrospective studies (or case-control studies). (lessons 31-35)
Complements of epidemiology: confounding; standardization; screening tests and diagnostic tests; sensitivity, specificity, predictive value of positive test result (PPV), predictive value of negative test result (VPN); relationship between prevalence, PPV and VPN. (lesson 36)

Fowler J. , Jarvis P., Chevannes M. Statistica per le professioni sanitarie Edises;

Paolo Chiari, Daniela Mosci, Enrico Naldi, Evidence-Based Clinical Practice. La pratica clinico-assistenziale basata su prove di efficacia 2/ed, McGraw-Hill

Deborah Morley and Charles S. Parker, Understanding Computers: Today and Tomorrow (16th edition) - Cengage Learning

Grassi R., Pinto G., Serra N. Sistemi per l’elaborazione dell’informazione Edises

Introduction to the world of computers: what is a computer, how computer works; terminology; main operations; a look at the computer history; The main types of computers; an introduction to hardware, main components: input, processing, output, memory and communication devices; an introduction to software: system software and application software; computer network and the Internet; computer and society. (lessons 1-3)
The language of computers: data and program representation; representing numerical data: the binary numbering system; coding systems for Text-Based Data and other types of data; Representing Software Programs: Machine Language. (lesson 4)
Hardware: inside the system unit: motherboard, CPU, GPU, memory, bus, expansion cards, ...; peripheral devices; the system clock and the machine cycle; strategies to improve the performance of a computer. (lessons 5-7)
Storage systems: storage systems characteristics; hard drives; optical discs and drives; flash memory storage systems; network and cloud storage systems; smart card; holographic storage; storage systems for large computer systems. (8-10)
Input and Output devices: pointing and touch devices; scanners, readers, digital cameras; audio input systems; display devices; printers; audio output devices. (lessons 11-13)
System Software (Operating Systems and Utility Programs): System Software vs. Application Software; functions of an Operating System; differences among Operating Systems; Operating Systems for personal computer and servers; Operating Systems for mobile devices and larger computer; utility programs: types and functions; the future of Operating Systems. (lessons 14-16)
Application Software: software ownership rights; desktop and mobile software, installed and cloud software; main types of application software: word processing, spreadsheet, database management systems, graphics and multimedia software, other types of application software. (lessons 17-20)
Database: introduction and definitions; entities and relationships; data definition; data dictionary; data integrity, data security, data privacy; data organization; types of DBMS; database models; relational model; tables, forms, queries, reports. (lessons 21-23)
Artificial Intelligence Systems: introduction and definitions; intelligent agents; expert systems; robotics. (lesson 24)
Information Systems and system development: responsibility for system development; outsourcing; the system development life cycle; approaches to system development. (lessons 25-26)
IT security: definitions; unauthorized access and unauthorized use; protecting against unauthorized access and unauthorized use; computer sabotage; protecting against computer sabotage; access systems based on the use of biometric data; firewall; encryption; private key cryptography; public key cryptography; virtual private networks (VPN); online theft, online fraud and other dot cons; protecting against online theft, online fraud and other dot cons. (lessons 27-30)
Exercises with spreadsheets: definitions and tools; basic operations; formulas; relative and absolute cell references; functions; statistical functions; date functions; text functions; nested functions; formatting; graphics and their customization; settings for printing; data transposition; cell comments. (lessons 31-36)

Fowler J. , Jarvis P., Chevannes M. Statistica per le professioni sanitarie Edises;

Paolo Chiari, Daniela Mosci, Enrico Naldi, Evidence-Based Clinical Practice. La pratica clinico-assistenziale basata su prove di efficacia 2/ed, McGraw-Hill

Deborah Morley and Charles S. Parker, Understanding Computers: Today and Tomorrow (16th edition) - Cengage Learning

Grassi R., Pinto G., Serra N. Sistemi per l’elaborazione dell’informazione Edises