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This course prepares engineering practitioners to produce systemic applied research or robust project solutions. In the applied research track, students will learn and apply the methods, tools, and concepts required for specifying the research purpose, proposal preparation, understanding the current state of the discipline, selecting and executing the appropriate research methodology, analyzing and synthesizing results, and preparing defensible publications. In the engineering project track, students will learn problem definition and scoping, project proposal preparation, methods for establishing the current state of the problem, analytical and experimental designs for testing proposed solutions, robust methods for solution optimization, and solution validation.

Introduction to modeling multivariate structural and residual variation, using exploratory data analysis, nonparametric regression, dependence regression, and factor analytic models, with a goal of producing robust, generalizable multivariate models that support research findings. Statistical analyses will be performed in the free general public licensed R statistical software with references to Minitab and SPSS.

The objectives of the course are to provide fundamental knowledge and skills of Big Data for the new generation of researchers, engineers, project managers and business managers in the emerging data-driven science and engineering paradigm. Topics to be covered include data analytics, cloud platforms and tools for Big Data, and innovative applications of Big Data.