Data Analysis

Data Analysis is a multidisciplinary field focused on interpreting large volumes of information to support decision-making, strategy development, and innovation. Statistical and machine learning techniques are employed to identify patterns and forecast future events, encompassing structured, semi-structured, and unstructured data.

For structured data, the main challenges involve analyzing time series and spatiotemporal data, including prediction, pattern discovery, and adaptation to data drift. Methods such as filtering and decomposition are used to build robust models for forecasting. The detection of events in time series, such as anomalies and regime changes, is relevant for both retrospective and real-time analysis.

When dealing with semi-structured and unstructured data, challenges include text mining and natural language processing (NLP). Text mining aims to uncover patterns and trends through statistical learning and text vectorization, supporting applications such as sentiment analysis and affective computing, which studies emotions in texts and human interactions. In this project, text mining is closely linked to affective computing and behavioral analysis, also encompassing image and video processing.

Behavioral analysis examines individuals within social networks, using graph-based models to identify communities and understand interaction dynamics. Applications include targeted marketing and information diffusion, providing insights into collective and emotional patterns within human interactions.

Faculty Members Involved:

• Eduardo Soares Ogasawara (coordinator) 
• Eduardo Bezerra da Silva 
• Gustavo Paiva Guedes e Silva 
• Jorge de Abreu Soares 
• Kele Teixeira Belloze