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
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.
The inLab FIB participates in the project for the creation of a Communication Coach for executives and companies together with the communication professionals of the company Commly.
The inLab FIB participates in an innovative project for the development of an interactive assistant based on Artificial Intelligence (AI), designed to facilitate the creation of extensive documents. This system will guide the user step by step in the elaboration of texts, ensuring coherence and precision.
This project seeks to develop a machine learning model to identify risk situations before starting climbing with self-insurer, helping the staff of the center to detect dangers automatically and precisely.