Data science research uses principles from computation, machine learning, statistics, and mathematics, to develop methods to analyze data and gain insight and knowledge about underlying systems to improve decision making. Data analytics is highly interdisciplinary with significant overlap with many areas, including human system integration, optimization, and stochastic systems.
This area includes:
Big Data Analytics: Techniques for analyzing large amounts of data to visualize patterns and discover the underlying principles governing the operational performance of industrial systems.
Predictive Analytics: Methods for predicting future outcomes, assessing and quantifying uncertainty about the future behavior of systems, and identifying the risk associated with decisions under varying environmental conditions.
Adaptive Learning: Fostering adaptive learning processes that use one or more sources of data collected over time to optimize dynamic decision-making and risk management.