Academic year
2021/2022 Syllabus of previous years
Official course title
Course code
EM1413 (AF:339173 AR:181542)
On campus classes
ECTS credits
Degree level
Master's Degree Programme (DM270)
Educational sector code
2nd Term
Course year
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Aim of this program is that of offering some scenario elements for one of the main application of data science: health systems. In parallel, it will be the opportunity to examine the more common statistical methodologies and tools in this field, both from a theoretical point of view and from a practical one through lab activities.
Knowledge on information systems for health systems. Analysis skills on statistical methods for health data
Statistical Learning for Data Science
1. Health systems information needs and (Statistical) Health Information Systems (class materials, case studies, discussion with professionals)
2. Statistical models for health data (ref. Agresti and Collett books)
- Analyzing contingency tables and comparing proportions. Relative risk, odds ratio and chi-squared test of independence
- Logistic regression. Interpretation, evaluation and selection. Categorical predictors and aggregated data.
- Loglinear models for contingency tables. Interpretation, evaluation and selection. Measures of association and independence. Relationship with logistic regression.
- Introduction to survival analysis. Survival function, Hazard function, censoring.
- Accelerated Failure Time models, proportional hazard models, Cox regression (brief reference)
3. Health data analysis lab
- Case studies and practical applications with R.
Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons. (ch 2, 4, 7)
Collett, D. (2015). Modelling survival data in medical research. CRC press. (ch 2 and 3)
For attending students: students will discuss a work group partially prepared during the lab part of the course
Non attending students: students will discuss a practical work on data given at the exam (written and oral)
Every week there will be one lesson for each of the three part of the course (Health Information Systems, Statistical Methods, Data Analysis Lab)
Students will be invited to participate actively to all the parts of the course: theory and practical aspects are equally important
There will be case studies and meetings with professionals

This subject deals with topics related to the macro-area "Poverty and inequalities" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Definitive programme.
Last update of the programme: 27/09/2021