MS Data Science, Analytics and Engineering (Bayesian Machine Learning)
This concentration in Bayesian machine learning within the MS program in data science, analytics and engineering is offered in partnership with the School of Mathematical and Statistical Sciences. With its programs in statistics, applied mathematics and theoretical mathematics, SoMSS is uniquely positioned to enable students to understand the statistical, probability and mathematical bases for the technical tools and emerging concepts in statistical and probabilistic machine learning and data science, and to support students' ability to collect, maintain, analyze, model and decide based on heterogeneous, time-dependent, noisy, biased, hierarchical and potentially large data sets. Bayesian thinking is particularly suited to addressing the difficult issues associated with these high dimension complex modeling challenges. Bayesian learning, decision-making, and computation have made a significant impact on many areas of data science. Hierarchical modeling, time series analysis, ensemble modeling, spatial modeling, and causal modeling are among the various areas of expertise covered by this program. Students will be able to apply these tools in a variety of domains such as engineering, physics, biology, social sciences, economics, and finance.
Students in this program perform Bayesian data analysis, modeling, remodeling, and decision making in data-enriched environments. This includes the exploratory analysis of massive and complex data-streams, Bayesian modeling and computing, data management, causal modeling and inference and decision under uncertainty using Bayesian trees, neural networks and text modeling popular in industry and academia.
This concentration in Bayesian machine learning within the MS program in data science, analytics and engineering is offered in partnership with the School of Mathematical and Statistical Sciences. With its programs in statistics, applied mathematics and theoretical mathematics, SoMSS is uniquely positioned to enable students to understand the statistical, probability and mathematical bases for the technical tools and emerging concepts in statistical and probabilistic machine learning and data science, and to support students' ability to collect, maintain, analyze, model and decide based on heterogeneous, time-dependent, noisy, biased, hierarchical and potentially large data sets. Bayesian thinking is particularly suited to addressing the difficult issues associated with these high dimension complex modeling challenges. Bayesian learning, decision-making, and computation have made a significant impact on many areas of data science. Hierarchical modeling, time series analysis, ensemble modeling, spatial modeling, and causal modeling are among the various areas of expertise covered by this program. Students will be able to apply these tools in a variety of domains such as engineering, physics, biology, social sciences, economics, and finance.
Students in this program perform Bayesian data analysis, modeling, remodeling, and decision making in data-enriched environments. This includes the exploratory analysis of massive and complex data-streams, Bayesian modeling and computing, data management, causal modeling and inference and decision under uncertainty using Bayesian trees, neural networks and text modeling popular in industry and academia.
