Base Knowledge
Not applicable.
Teaching Methodologies
In the classes the expository method will be used, with demonstration and discussion. Some of the problems to be addressed will allow the use of computational tools such as Python, R or spreadsheet language.
Learning Results
Understand health data management and governance processes
Understand data analysis methodologies and processes
Understand the different types of data, features and tools
Understand the main methods of data analysis, advantages, limitations and risks
Program
1. Data governance. GDPR.
2. Professions in data management and analysis.
3. Data analysis process and CRISP-DM methodology.
4. Health data-imaging, genetic codes, administrative data and analysis tools.
5. Analysis and coding of clinical text.
6. Image analysis.
7. Analysis and prediction models in time series.
Curricular Unit Teachers
Mateus Daniel Almeida MendesGrading Methods
The evaluation will consist of one or more research papers, which must be carried out according to the interest of the students, within the scope of the topics covered in the curricular unit. Works are subject to mandatory presentation and defense.
Internship(s)
NAO
Bibliography
John D. Kelleher and Brendan Tierney (2018) Data Science (The MIT Press Essential Knowledge series), MIT Press, ISBN-13: 978-0262535434.
John Kelleher, Brian Mac Namee, and Aoife D’Arcy (2015); Fundamentals of Machine Learning for Predictive Data Analytics; The MIT Press, ISBN: 9780262044691.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor (2023) An Introduction to Statistical Learning with Applications in Python, Springer, ISBN 9783031387463.
Rob J Hyndman, George Athanasopoulos (2021) Forecasting: Principles and Practice, OTexts, ISBN: 9780987507136.