Class Data Processing in e-Health

This Curriculum Unit (UC) aims to promote skills in the following processes: data collection for human behavior assessment, statistical analysis of raw data, data detection and cleaning, and continuous data update. During the semester, students will work on comprehensive and realistic projects, applying all these steps.

Not applicable

PC 1.Strategies for data processing  1.1.Operational efficiency; 1.2.Process optimization; 1.3.Decision making; 1.4.Client satisfaction PC 2.Data collection: 2.1.Quantitative 2.2.Qualitative PC 3. Data aggregation 3.1.In-network approach 3.2.Tree-based approach 3.3.Cluster approach 3.4.Multi-path approach PC 4.Data cleaning: 4.1. Removal 4.2.Standardization 4.3.Conversion; 4.4.Translation 4.5.Missing values PC 5.Data updating 5.1.Single updates 5.2.Batch updates

Being a practical unit, the experiential methodology will be followed. The students will, across the semester, be invited to engage in a set of exercises that will mimic a ‘real’ procedure, requiring the use of all data processing steps. The work will be carried out in teams of two to allow the exchange of ideas and to promote collaborative work.

Clarke, E. (2022). Everything Data Analytics-A Beginner's Guide to Data Literacy: Understanding the Processes That Turn Data Into Insights. Kenneth Michael Fornari. Soleil, O. & Jelen, B. (2022). Guerrilla Data Analysis Using Microsoft Excel: Overcoming Crap Data and Excel Skirmishes. Holy Macro! Books.  Long, J. (2019). R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. O'Reilly Media. Shaee, A.B.R., Hutchinson, D.M., Teague, S.J. (2019). Machine learning in mental health: a scoping review of methods and applicaSons. Psychol Med. 49(9):1426-1448. doi: 10.1017/S0033291719000151