Research lines

The subject of study of the research group on Fundamentals and Applications of Data Science (DSLAB) is the Science of Data, its foundations and its applications. Data Science is a combination of Mathematics and Statistics, Engineering, Information Technology and domain knowledge. The main objective of the DSLAB is to study and develop the necessary tools, knowledge and skills to guarantee the correct development of a Data Science project. It is intended, on the one hand, to study and develop new statistical techniques and of machine learning to perform Data Science. In addition, the analytical applications necessary to improve the daily practices of experts from different application domains are designed and evaluated.

DSLab

In the area of ​​Data Engineering, storage, representation, transformation, computation and parallelization are studied for large volumes of data. It is responsible for the development, construction, testing and maintenance of Big Data architectures and technologies. Once continuous pipelines are available to and from the information, data analysts can carry out their analyzes. Within this area we consider two subareas: Computer Science & Information Systems and Process & Software Engineering Quality.

Computer Science & Information Systems

  • Data adquisition
  • Data storage
  • Data cleaning
  • Data preparation
  • Computation and parallelization

Process & Soft. Engineering Quality

  • Technologies and computing resources
  • Software Engineering
  • Data management

In the area of ​Data Analytics, models, classification, prediction, and visualization associated with the data are studied. It is the area in charge of the design and elaboration of algorithms and mathematical and statistical models to extract valuable information from the data. Within this area we consider two subareas: Statistics & Machine Learning and Optimization & Maths.

Statistics & Machine Learning

  • Pattern recognition
  • Machine Learning algorithms
    • Supervised
    • Unsupervised
    • Semi-supervised

Optimization & Maths.

  • To study and provide the Data Analysis area with the most suitable optimization algorithms in each case of study.