Introduction

These are the times of Big Data, in which different technologies produce thousands of terabytes of data every minute. In the life sciences, this information is important in the areas of health and agrofood.

By processing these huge volumes of data together it is possible to generate new knowledge to help us better understand diseases, identify new therapeutic pathways and accelerate the translation of new discoveries to use in patient care, thus advancing towards personalised medicine and a more efficient healthcare system.

Generating new knowledge and technologies from Big Data is a process that consists of several stages. Although we tend to focus our attention on the data analysis phase, the other stages are also important: extraction, integration, visualisation or modelling. Some factors that need to be taken into account in this complex process are heterogeneity, scalability, response times and data protection.


In order to improve our understanding of systems and our decision-making in the life sciences it is important to train up professionals who are familiar with the new strategies, methodologies and tools for processing and analysing data.

Big Data is the name given to the data sets, procedures and software that, due to their volume, varied nature and the speed at which they need to be processed, are beyond the capacity of ordinary information systems.

Big Data is giving rise to new advances in technology and is allowing production costs to be lowered. The real value of the data lies in the new information and knowledge that can be extracted from it.

This is of great relevance to the life sciences, as large amounts of data are being produced and stored that can lead to advances in fields such as personalised medicine or the -omic sciences (genomics, transcriptomics, proteomics, metabolomics, etc.), new systems for classifying pathologies, and new methods and models to improve home telecare and the monitoring of dependent persons who are receiving pharmacological treatment.

The processing all this data must be done by interdisciplinary teams of professionals from different areas of knowledge.

Objectives

- Train students to become specialist professionals in the processing and analysis of data related to life sciences. Analyse the information and computing requirements in the area of life sciences and identify solutions at all stages.
- Acquire, prepare, store, view and manage structured and non-structured information to improve knowledge and decision-making in the area of life sciences.

- Understand techniques for modelling, analysis and viewing of big data and smaller volumes though the study of theory and practice.
- Familiarise themselves with the software architecture for implementing big data solutions.
- Manage the ethical and legal implications of the use of data (privacy, security and data protection).

Access requirements

The graduate diploma is aimed mainly at graduates in the following areas:
- Technology, e.g. computer engineering
- Health sciences, e.g. medicine, nursing, or physiotherapy
- Science and bioscience, e.g. biology or bioinformatics

Career opportunities

The graduate diploma offers training for work in the field of structured and unstructured data processing in the life sciences:
- Data scientist.
- Data analyst.
- Designer of data exploitation systems.
- Big-data architect within the life sciences.

Contact

Dolores Isabel Rexachs del Rosario

Phone: 935813535

E-mail: Dolores.Rexachs@uab.cat

UAB Graduate Diploma

1 Edition
Places: 25
Orientation: Academic
35 ECTS
Code: 3695/2
Price: 3800 ¤

See price details

Modality: Onsite
Location: Escola d'Enginyeria, Campus UAB, Bellaterra (Barcelona)
Dates: from 16/10/2017 to 21/03/2018
Teaching language:
Spanish (50%), Catalan (30%), English (20%)
Coordinating centres:
Escuela de Ingeniería
Departamento de Arquitectura de Computadores y Sistemas Operativos
Collaborating centres:
Associació Bioinformatics Barcelona (BIB)
Centro de Regulación Genómica (CRG)
Hospital Sant Joan de Deu
Hospital Universitari de Bellvitge
IBM
I2 cat - Fundació Internet i Innovació a Catalunya
IDIAP Jordi Gol - Institut d'Investigació en Atenció Primària Jordi Gol
Microsoft
ENG/Consorci Hospitalari Parc Taulí

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