This interdisciplinary program focuses on the analysis and handling of data from multiple sources and for various applications in order to draw inferences from it, combining topics from mathematics, statistics, and computer science. These topics include probability theory, inference, least-square estimation, maximum likelihood estimation, finding local and global optimal solutions (gradient descent, genetic algorithms, etc.), and generalized additive models. It also covers machine learning topics such as classification, conditional probability estimation, clustering, and dimensionality reduction (e.g. discriminant factor and principal component analyses), and decision support systems. The program also covers big data analysis, including big data collection, preparation, preprocessing, warehousing, interactive visualization, analysis, scrubbing, mining, management, modeling, and tools such as Hadoop, Map-Reduce, Apache Spark, etc.