Data Science
Description of Data Science
In the Data Science, we are experts in data-driven decision making, i.e. data management and analysis.
Data management: Collection, storage, integration and processing of data.
- From where? Own and third party data (Open Data, Internet of Things, etc.).
- System architectures based on Big Data (distributed systems), Data Warehousing.
- AdvancedTechniques : NOSQL Techniques, Data Vectorization, Big Data Integration, Big Data Quality, etc.
Data analysis: data preparation for analysis, feature generation, model learning/creation, model validation, interpretation and visualization.
- Type of analysis: descriptive (query & reporting, OLAP, descriptive statistics, graph analytics), predictive (data mining, machine learning, process-oriented data science, streaming, etc.).
- Advanced techniques: explanation of outcome, causality, ethics and equity.
- Visualization: dashboards, what-if analysis, geospatial, temporal, networks, advanced visual analytics, etc.
Data Science Success Stories
inLab FIB and Qorvo Inc. are collaborating on a research project exploring artificial intelligence-based approaches to accelerate microelectronic circuit design, with a focus on design automation and performance prediction.
The inLab FIB will carry out a study and develop a proof of concept of a new triage system for the “Hospital de la Santa Creu i Sant Pau”. This system will prioritize certain patients to prevent waitings from negatively affecting them.
The inLab FIB participates in a innovative project to develop an interactive assistant based in Artificial Intelligence (AI), designed to help solve quality issues.
This project aims to implement Industry 4.0-based technologies to make communications, data collection and supervision of unconnected machinery within a medical/ hospital environment.
In today’s digital age, mobile applications have become an integral part of our daily lives, but they have also increased the risks of cybersecurity. Companies face the challenge of protecting their applications and user data in an environment of constantly evolving threats.
The goal of this project is to develop a technological tool to measure, analyze and reduce the environmental impact within the industrial environment.