2024
Big Data in Health Sciences and Technologies
Name: Big Data in Health Sciences and Technologies
Code: ENF13716D
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Informatics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Presentation
The UC enables the analysis of concepts and methodologies associated with the big data area and its application to the health domain, through the main approaches and computational tools that represent the state of the art.
Sustainable Development Goals
Learning Goals
This curricular unit aims to analyse the concepts and methodologies associated with the big data area and its application to the health domain.
In addition to a theoretical approach, the main approaches and computational tools representing the state of the art will be presented, discussed and evaluated. Specifically, the problems associated with the collection, transformation, analysis, visualization and interpretation of large volumes of data in the health area will be addressed, with several case studies being carried out.
At the end of this course students will have:
- knowledge of big data concepts and methodologies;
- ability to apply the main methodologies, being aware of their potential and limitations;
- ability to define big data strategies in the health area.
In addition to a theoretical approach, the main approaches and computational tools representing the state of the art will be presented, discussed and evaluated. Specifically, the problems associated with the collection, transformation, analysis, visualization and interpretation of large volumes of data in the health area will be addressed, with several case studies being carried out.
At the end of this course students will have:
- knowledge of big data concepts and methodologies;
- ability to apply the main methodologies, being aware of their potential and limitations;
- ability to define big data strategies in the health area.
Contents
- Big Data: fundamentals and basic concepts;
- Big data in the health area: heterogeneous and semi-structured data (medical records, exams, IoT); ethical and legal issues;
- Main processes in big data analysis: collection, cleaning, transformation, analysis, visualization, interpretation;
- Automatic learning approaches for classification, grouping / clustering and identification of correlations in the data.
- Construction of explanatory and predictive models.
- Solutions in the "cloud" and frameworks for "big data analysis"
- Case studies: analysis and implementation of solutions.
- Big data in the health area: heterogeneous and semi-structured data (medical records, exams, IoT); ethical and legal issues;
- Main processes in big data analysis: collection, cleaning, transformation, analysis, visualization, interpretation;
- Automatic learning approaches for classification, grouping / clustering and identification of correlations in the data.
- Construction of explanatory and predictive models.
- Solutions in the "cloud" and frameworks for "big data analysis"
- Case studies: analysis and implementation of solutions.
Teaching Methods
As a teaching methodology, a mixed approach will be made with theoretical presentations of the concepts complemented by concrete applications developed individually by the students.
The evaluation will be carried out through the resolution of case studies, including its presentation and discussion before the teacher.
The evaluation will be carried out through the resolution of case studies, including its presentation and discussion before the teacher.
Certificações
