Data Science

General objectives. Digital data sources and digital traces of human behaviour have the potential to provide local and timely information about disease and health dynamics at the population level. The general aim of the course is to introduce students to the analysis of epidemiological and omics data and to the use of computational approaches for medical/clinical purposes. Specific objectives. The course consists of two modules. The first module will deal with the opportunities and challenges of mining digital data sources for epidemiological and public health signals and will provide an overview of the state of the art of this emerging field. The second module will focus on “precision medicine”, an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. With the second module, the students are expected to acquire basic biology knowledge and skills and to become familiar with the analysis and integration of omics data. Knowledge and understanding. The course will include theory and hands-on lectures. Students will be trained in the basic theory for the identification of gene interactions and in the use of network science. Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts, with the analysis of omics and epidemiological data and with the use of networks for the investigation of infectious disease dynamics and disease etiology, diagnosis, and treatment. Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis. Communication skills. The students will be required to produce reports describing hands-on projects with specific sections for the description of the obtained results and their discussion. Learning ability. The projects will be developed in small groups encouraging team building.