Applied Data Analysis and Modeling for Energy Engineers and Scientists

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Models, data analysis and decision making. See All Customer Reviews. Shop Textbooks. Add to Wishlist.

USD Sign in to Purchase Instantly. Applied Fuzzy Arithmetic. Applied General Equilibrium. Applied Geothermics.

Applied Hydraulic Transients. Applied Hydrogeology of Fractured Rocks. Applied Hydrogeophysics. Applied Hydrometeorology. Applied Immunohistochemistry in the Evaluation of Skin Neoplasms. Applied Impulsive Mathematical Models. Applied Informatics and Communication. Applying a Personalised Approach to Eligibility Criteria.

Concept and computation of equilibrium in n-person games. Selected topics from the fields of linear programming, nonlinear programming, dynamic programming, heuristics, and constraint programming. May be repeated for credit provided the topic differs. Combinatorial optimization problems: algorithms and applications. Network problems: minimum spanning tree, shortest path, maximum flows, minimum cost flows, optimal matchings, routing problems. Complexity theory. Enumeration and cutting plane methods for solving integer programs.

Systems Thinking and Policy Modeling I. Introduction to systems thinking and the system dynamics approach to policy analysis, with applications to business management and public policy. Causal-loop and stock and flow models of business growth, technology adoption, and marketing. Use of role-based games to explain key principles of systems. Use of simulation software to model problems and case studies. Case studies in dynamic policy analysis. Use of microcomputers in simulation. The class collectively models and simulates a social system to explore policy options.

Stochastic Foundations of Operations Research. Topics in probability theory, stochastic processes, and statistical inference. Foundations of probability, conditional probability and expectation, Poisson processes, Markov chains, and Brownian motion.

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Statistical approaches to quality assurance. Single and multivariate control charts, acceptance sampling by attributes and variables, process capability and design of experiments. Analytical methods for the solution of problems in engineering using concepts from probability and statistics: probability modeling, random variables and their distributions, mathematical expectation, sampling, point and confidence interval estimation, hypothesis testing, correlation, regression, and engineering applications.

Restricted to SEAS graduate students. Design of experiments and data collection. Regression, correlation, and prediction. Multivariate analysis, data pooling, data compression. Model validation. Applied and practical data analytics. High-level theory, with primary focus on practical application of a broad set of statistical techniques needed to support an empirical foundation for systems engineering and engineering management.

Steps to Become a Data Engineer

A variety of practical visualization and statistical analysis techniques. Leveraging Minitab and Excel to examine raw data to arrive at insightful conclusions. Topics and models in current risk analysis; modern applications of risk-based planning and risk management; use of quantitative methods in risk analysis. Quantitative methods in model building for logistics systems, including organization, procurement, transportation, inventory, maintenance, and their interrelationships. Stresses applications.

How to Become a Data Engineer

Systems approach to the architecting and engineering of large-scale systems; elements of systems engineering; methods and standards; computer tools that support systems and software engineering; trends and directions; the integrative nature of systems engineering. Application of systems engineering tools to provide hands-on experience with essential elements of practice.

Processes of requirements engineering, functional analysis and allocation, risk management, architecting; architectural heuristics, axiomatic design, analytical assessment of alternative architectures. Applications of systems engineering tools and techniques. The systems or holistic approach as a methodology for making decisions and allocating resources.

Analysis by means of objectives, alternatives, models, criteria, and feedback. Requirements in systems engineering, including requirement types, quality factors, elicitation methods, analysis, derivation of implicit requirements, management, traceability, verification, cross-requirement assessments, and validation.

Focus on writing and managing quality requirements in complex systems. Model-based systems engineering MBSE and its derivative, evidence-based systems engineering EBSE , are techniques with strong potential for improving the technical integrity of complex systems. The foundation to these model- and research-based techniques for system definition and analysis as applied to life-cycle SE.

Practical applications. Problems in managing projects; project management as planning, organizing, directing, and monitoring; project and corporate organizations; duties and responsibilities; the project plan; schedule, cost, earned-value and situation analysis; leadership; team building; conflict management; meetings, presentations, and proposals. Project Cost and Quality Management. Developing project cost and resource estimates during the planning stages. Monitoring, forecasting, and controlling cost throughout the project life cycle. Project quality planning, assurance, and control.

Relationships among project scope, time, cost, quality, human resources, communications, procurement, and risk. Preparation for the Project Management Professional examination. Study of the human—machine interface applied to system design, job design, and technology management. Human sensory—motor, perceptual, and cognitive functions; task analysis and allocation; contextual aspects of human factors engineering.

Modeling, design, and evaluation methodologies. Applications to user-centered industrial and information systems. Applied Enterprise Systems Engineering. Applications of systems engineering in the U.

Lean and Agile Systems Engineering. Lean and agile methods as applied to the engineering design and development of systems; review of contemporary implementation frameworks, methodologies, and the tools used to support them. Implications for traditional systems engineering; fundamental changes to the requirements processes; implications for engineering management. Complex systems engineering in terms of systems of systems SoS ; theoretical and practical instances of SoS; application of lifecycle systems engineering processes; various types of SoS and the challenges to be faced to ensure their acquisition and technical integrity.

Quantitative modeling techniques and their application to decision making in systems engineering. Linear, integer, and nonlinear optimization models. Stochastic models: inventory control, queuing systems, and regression analysis. Elements of Monte Carlo and discrete event system simulation.

Reliability Analysis and Infrastructure Systems. Modeling basic variables and defining the limit—state surface. Modeling an infrastructure system. Reliability analysis using branch and bound, failure paths and failure modes, identification of dominant failure paths.

Limited to students in the Applied Scientist or Engineer degree program. Selected topics in engineering management and systems engineering, as arranged. May be repeated for credit. Permission of the instructor required prior to enrollment. Basic or applied research in engineering management or systems engineering. Advanced Topics in Operations Research. Advanced topics from the literature of operations research for analysis, presentation, and discussion.

Reading assignments from professional journals selected by the instructor and the student. Prerequisite: permission of instructor. First in a two-course sequence of doctoral seminars designed to give students their first exposure to the process of formulating and executing empirical research. Class format includes discussion, field experiments, data analysis, and theorizing. Study of core concepts in building theory from empirical data and classic works in technically-oriented management theory.

Participants design and execute a research project. Second in a two-course sequence introducing doctoral students to the fundamentals of research design and methods. Introduction to a range of research methods relevant to the study of engineering management and systems engineering, reading, writing, and critiquing the elements of a research proposal. Advanced Stochastic Models in Operations Research. Applied probability models, including the Poisson process, continuous-time, denumerable-state Markov processes, renewal theory, semi-Markov regenerative processes.

Applications to queues, inventories, and other operations research systems. Risk Management Process for the Engineering Manager. Risk management process; individual and collaborative responsibilities of program and engineering managers; practical applications of risk-based planning and risk management tools essential to success of any program; communicating the process and its value in avoiding catastrophic outcomes.

Survey of Research Formulation for Engineering Management. Researching the praxis paper. Introduces the design of research studies in applied engineering management settings from a practical perspective. Restricted to students in the DEng in the field of engineering management program.

Overview of research methods; aims and purposes of the praxis; development of praxis research strategies; formulation and defense of a praxis proposal. Praxis proposal defense must be passed before the student is admitted to degree candidacy to undertake praxis work. Restricted to students who have completed all required coursework for the DEng in the field of engineering management degree.

Independent applied research in engineering management culminating in the final praxis report and final examination for the degree of doctor of engineering. Restricted to students in the DEng in the field of engineering management program who have passed the praxis proposal defense. Advanced Reading and Research. Restricted to doctoral candidates. The George Washington University. Toggle Navigation Toggle Navigation. Operations Research Methods.

Data Modeling & Analytics

Discrete Systems Simulation. Special Topics. Engineering Economic Analysis. Applied Optimization Modeling. Engineering Law. Technology Issue Analysis. Entrepreneurship and Technology. Technical Enterprises. Marketing of Technology.

Admission Requirements

Environmental Management. Air Quality Management. Water Quality Management. Environmental Hazard Management. Energy Management. Greenhouse Gas Mitigation. Environmental Secuity. Crisis and Emergency Management. Knowledge Management I. Knowledge Management II. Advanced Knowledge Management. Information Operations. Cyber Resilience. Programming for Analytics. Data Mining and Processing. Data-Driven Policy.