Databases and exposure scenarios

"Informatics and Databases"
Introduction to databases. Information systems, information, and data. Database and Database Management System (DBMS). Data models. Schemas and instances. Abstraction levels in DBMSs. Database languages and users.
Relational databases. The relational model. Relations and tables. Relations with attributes. Relations and databases. Incomplete information and null values. Integrity constraints. Definitions and properties of keys. Primary key and foreign key constraints.
Query languages for relational databases: SQL. Basic SQL query format. Selection and projection queries. Join queries (inner join, natural join, outer joins). Aggregate queries. Group by queries. Set (union, intersection, difference) queries. Nested queries. Correlated nested queries.
Conceptual data modeling with the Entity-Relationship model.
Working with a real database example storing protein-related data. Online access to the database schema and constraints. Interactive formulation of SQL queries over the online database.

"Statistics applied to epidemiology"
Introduction to the course (objectives, final evaluation criteria). All of the following theoretical topics are accompanied by examples.
Collecting data sets: populations and samples. Describing data sets: frequency tables and graphs. Summarizing data sets: mean, median, mode. Outliers. Variability: variance, standard deviation. Describing data sets: sets of paired data, scatter diagram, least squares regression line, slope (linear regression coefficient), intercept. Qualitative and quantitative evaluation of linear regression. The correlation coefficient. Interpreting regression and correlation.
Probability. Definitions: experiment, outcome, sample space, event. Rules of probability and independent events. Conditional probability and Bayes Theorem. Probability distribution, expected value. Binomial, Gaussian, t-Student, Fisher and chi-squared distributions.
Standardizing normally distributed random variables. Population and sample. Population mean and variance. Sample mean, expected value of the sample mean, variance of the sample mean, standard deviation of the sample mean. Central limit theorem.
Epidemiological indicators: prevalence and incidence, absolute and relative risks, odds ratios and hazard ratios. Confidence intervals. Statistical inference, null hypothesis and statistical significance. Student's t test. Chi-squared test, non-parametric tests.
Main sources of bias and their relation with accuracy and precision. True positives, true negatives, false positives, false negatives. Sensitivity, specificity and ROC curve. Graphical representation.
Types of epidemiological studies. Observational studies: cross-sectional, case-control, cohort. Experimental studies: randomized controlled trials, field trials, community trials. Potential limitations in epidemiological studies.
Main criteria for evaluation of scientific studies with special reference to statistical studies applied to epidemiology. Understanding the statistical methodology, the results, and their interpretation with examples from scientific publications.
Review of the whole program with exercises on the board.