Contents
Data analysis has been intensively and extensively used by many organizations. In the life sciences, clinical data analysis is becoming increasingly popular, and it could even be said that it is becoming essential.
The application of methodologies based on AI and Big Data can greatly benefit all the stakeholders involved in the healthcare sector. For example, data analysis can help healthcare organizations in management decision-making, assist doctors in identifying effective treatments and best practices, and enable patients to receive better healthcare services at a lower cost.
The enormous amount of data generated by healthcare is too complex to be processed and analyzed using traditional methods. Artificial intelligence applied to data analysis provides the methodology and technology to transform these vast amounts of data into useful information for decision-making.
Masters degree studies are organized into 5 compulsory courses, each worth 10 ECTS, and a Master's Final Project (10 ECTS).
| Subject | Credits | Type |
|---|---|---|
Artificial Intelligence in Health |
10 ECTS |
Obligatory |
Health Data Analysis |
10 ECTS |
Obligatory |
Big Data Environments for Data Analysis |
10 ECTS |
Obligatory |
Health Data Acquisition, Filtering And Security |
10 ECTS |
Obligatory |
Data Storage and Visualization |
10 ECTS |
Obligatory |
Itinerary
The Master's in Artificial Intelligence and Big Data in Health can be pursued through two options:
- Enroll in the entire master's program
- Enroll in the intermediate studies of the program: enroll in each course individually, and once all are completed, enroll in the remaining credits and the master's thesis.
Related programmes
Graduate Diploma in Artificial Intelligence and Big Data in Health
Course in Artificial Intelligence Iin Health
Course in Big Data Environment For Data Analysis
Course in Health Data Acquisition, Filtering And Security
Course in Health Data Analysis
Course in Health Data Storage And Viewing
Specific skills
KNOWLEDGE:
KT01: Recognize the technologies for generating health data.
KT02: Identify AI-based environments and the models used.
KT03: Identify specific technologies and concepts in the field of big data.
KT04: Demonstrate knowledge in AI and Big Data environments/applications/models in Health.
SKILLS:
ST01: Analyze data transformations and processing in the field of Health Sciences.
ST02: Relate all aspects linked to data in the field of Health Sciences.
ST03: Analyze AI methodologies applied to medical data.
ST04: Use environments and tools for managing big data.
ST05: Determine efficient data management environments and structures.
ST06: Experiment with AI and Big Data tools and models.
COMPETENCIES:
CT01: Evaluate different aspects related to data in the field of Health Sciences.
CT02: Design environments for medical data processing.
CT03: Validate technological tools for big data management. CT04: Design big data processing code.
CT05: Build AI and Big Data-based processing environments.
Title obtained
Lifelong Learning Master's Degree Artificial Intelligence And Big Data in Health