Data Science

Data Science (Data Science) is an interdisciplinary area that investigates models, methods, processes, algorithms and tools that allow extracting knowledge and discoveries from heterogeneous data (structured or not), usually in large volume and arising from from different sources.

Currently, this area comprises the following lines of research:

Algorithms and Data Structures for Data Science

The datasets we currently have to deal with are characterized by their large volume, variety and speed of production and acquisition. These characteristics bring new challenges for data modeling, processing and analysis. In this line, efficient algorithms and data structures are investigated to face these challenges.

Visual and exploratory data analysis

Visual and exploratory data analysis seeks to combine the strengths of human perception and cognition with the processing power strengths of computer systems. In this line, forms of visualization and interaction with data are investigated, as well as environments that support the activity of visual analysis.

Frameworks and Tools for Data Science

Working with data involves capturing heterogeneous data from different sources, storing it, adjusting it, visualizing it, creating models and applying methods for analysis. There is a great demand for flexible, configurable and integrated tools to support the various activities involved. In this line, such tools are investigated, from their conception to their development.

Methods and Models for Data Science

With the increasing availability of heterogeneous, massive, and streaming data, existing approaches to data modeling and analysis may become inadequate. In this line of research, new methods and models are investigated to overcome the limitations of current approaches and deal more effectively with these large volumes of data.