Teaching plan for the course unit



Close imatge de maquetació




General information


Course unit name: Cičncia de Dades i Salut

Course unit code: 574186

Academic year: 2020-2021

Coordinator: Laura Igual Muńoz

Department: Faculty of Mathematics and Computer Science

Credits: 3

Single program: S



Other contents



Deep Learning with Electronic Health Record (EHR) Systems:


The Data Science of Health Informatics:


Introduction to Clinical Data Science:


Program Health Information Literacy for Data Analytics:


Data Analytics and Visualization in Health Care:




Estimated learning time

Total number of hours 75


Face-to-face and/or online activities



-  Lecture with practical component





(face-to-face and on-line)

Supervised project


Independent learning




Competences to be gained during study


Ability to apply the knowledge acquired to develop and defend arguments, and to solve problems related to data science for health, often in a research context.

Ability to gather and interpret relevant data to make judgments that include reflection on important issues related to data science for health.

Ability to work independently and make decisions.

Ability to find rlevant information by accessing bibliographic databases.





Learning objectives


Referring to knowledge

This course will focus on specific methods for data collection and preparation, data analytics methods and tools, as well as how to generate and communicate meaningful insight from analytics. 



Teaching blocks


1. Introduction

2. Representation learning

3. Information extraction

4. Clinical predictions

5. Medical images

6. Emerging themes

7. Privacy and ethical issues

8. Clinical applications



Teaching methods and general organization


Due to the health emergency of COVID-19 and during the 2020-2021 academic year, the subject will follow a model of blended learning instead of face-to-face. The division of students into groups will be adapted to this situation.  Weekly teaching will be structured as follows:

  • 50%: A non-face-to-face block (synchronous or asynchronous) following a theoretic-practical approach.
  • 50%: A face-to-face block corresponding to theoretical-practical activities. These classes are devoted to review theoretical concepts and their practical applications.

If the health situation allows it, it would move to a face-to-face teaching format. In this case, the hourly load remains at the same values, but all classes are held in face-to-face mode, and the distribution of students in groups can be varied to suit the face-to-face mode.

Likewise, all the sessions could be adapted to non-attendance in case the health situation required it.

The activities are proposed and followed through the Virtual Campus.



Official assessment of learning outcomes


Problem Sets: 100%.


Examination-based assessment

Problem Sets: 100%.



Reading and study resources

Consulteu la disponibilitat a CERCABIB


“Data Science for Healthcare: Methodologies and Applications”, Consoli, Sergio, Reforgiato Recupero, Diego, Petković, Milan. Springer 2019.