Earning Criteria

• Badge earners complete SE-5702 Data Science for Materials and Manufacturing course at the University of Connecticut, which is a hybrid-online graduate course that can be taken from anywhere in the world. Earners can take this graduate course as a matriculated UConn graduate student or as a non-degree graduate student, which does not require admission to the UConn graduate school. Badge holders complete a course-long project and must earn a B- or better on this project to earn the badge.
• Badge earners can apply systems engineering methods and principles to the design and operation of cyber-physical systems, can apply machine learning methods and practices to the design and operation of cyber-physical systems, can apply knowledge of how cyber-physical systems methods integrate at the large, meta system level, and can create and update cyber-physical systems models using real-time operating data. See Standard [1] NAE-CPS below.
• Badge earners can describe and characterize uncertainty, statistical inference, detection, and estimation in models and analyze their effects on cyber-physical systems behavior, can conduct sensitivity analysis for a complex system using a model, can quantify uncertainty in a complex system using a model, and can develop and use systematic methodologies for the estimation of system parameters, constitutive relations and uncertainties based on data. See Standard [2] DOE-SIAM below.
• Badge earners can incorporate observational and experimental data to model and simulate a complex system, can develop sound, computationally feasible strategies and methods for the collection, organization, statistical analysis, and use of data associated with complex systems, and can quantify the effects of uncertainty and numerical simulation error on predictions using complex models and when fitting complex models to observations. See Standard [2] DOE-SIAM below.
• Badge earners can perform modeling and analysis of a large stochastic system and simulate to understand performance based upon technical performance measures and can optimize a complex system to meet stakeholder requirements and best engineering practice standards. See Standard [2] DOE-SIAM below.
• Badge earners can develop and use approaches for deriving computationally tractable approximations to systems that are formulated in very high dimensional spaces, such as those arising in quantum mechanics, and can develop and use systematic mathematical approaches for constructing nonlinear empirical models informed by physics principles, possibly including physically imposed constraints. See Standard [2] DOE-SIAM below.
• Badge earners can perform modeling and analysis to determine optimal properties of materials based upon historical material and process data and can develop analytical and computational approaches needed to understand and model the behavior of complex multi-physics, and multiscale phenomena. See Standard [2] DOE-SIAM below.
• Badge earners can identify design attributes and explain why attributes must be balanced using tradeoff studies, can explain how the design, throughout the lifecycle, affects the robustness of the solution, can identify design attributes and describe how they influence the design, can use techniques and tools to ensure delivery of designs meeting specialty needs, and can explain how own perception of arguments from others may be biased and how to recognize. See Standard [3] INCOSE ISECF below.
• Badge earners will have completed a course project applying data science techniques to a materials or manufacturing problem working individually or in a group of two persons. Deliverables include a mid-term progress update presentation, a final presentation, and a final report.