About us
The Data Science Lab is a high performance research group in Foundations and Applications of Data Science, based at University Rey Juan Carlos (Madrid, Spain).
The group pursues three main objectives:
As previously mentioned, DSLAB focuses on the Science of Data, encompassing its foundations and applications. Data Science is an interdisciplinary field that integrates Mathematics and Statistics, Engineering, Information Technology, and domain-specific knowledge. The main objective of the group is to research and develop the necessary tools, knowledge, and skills required to ensure the successful development of Data Science projects. This involves studying and creating innovative statistical and machine learning techniques for Data Science. Additionally, DSLAB designs and evaluates analytical applications to enhance the daily practices of experts across various fields.
This study distinguishes two main groups or tasks: Data Engineering and Data Analytics.
Data Engineering
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 analyses. Within this area we consider two subareas: Computer Science & Information Systems and Process & Software Engineering Quality.
Computer Science & Information Systems
- Data acquisition
- Data storage
- Data cleaning
- Data preparation
- Computation and parallelization
Process & Software Engineering Quality
- Technologies and computing resources
- Software Engineering
- Data management
Data Analytics
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.
