This course provides students with the background necessary to model, store, manipulate, and exchange information to support decision making. It covers Unified Modeling Language (UML), SQL, and XML; the development of semantic models for describing data and their relationships; effective use of SQL; web-based technologies for disseminating information; and application of these technologies through web-enabled database systems. Prerequisite: Systems Major; SYS 2001 and CS 2110, or Instructor Permission

Students learn about the practice of systems engineering directly from practicing systems engineers. A variety of topics are covered by invited speakers from industry, government, and the academy (many of whom are alumni of our undergraduate program). Discussions include engineering design projects, alternative career paths, graduate studies, professional development and advancement strategies, and more immediate options and opportunities for summer internships and capstone projects. Prerequisite: Third-year standing in systems engineering.

A design project extending throughout the fall semester. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience. Prerequisite: SYS 3021, 3060, and fourth-year standing in the Systems Engineering major.

A design project extending throughout the spring semester. Involves the study of an actual open-ended situation, including problem formulation, data collection, analysis and interpretation, model building for the purpose of evaluating design options, model analysis, and generation of solutions. Includes an appropriate computer laboratory experience. Prerequisite: SYS 4053.

This is a colloquium that allows fourth-year students to learn about engineering design, innovation, teamwork, technical communication, and project management in the context of their two-semester systems capstone design project. With respect to their capstone project, students define and scope their project, structure an interim report about the project, and give an oral presentation to the class. In addition, students study methods of effective time management and prepare presentations of their 5-year career plans. Prerequisite: Fourth-year standing in systems engineering.

Detailed study of a selected topic determined by the current interest of faculty and students. Prerequisite: As specified for each offering.

Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system's response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining. Prerequisite: SYS 6021, SYS 4021, or STAT 5120.

This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. Coursework is conducted in the R programming language.

Detailed study of graduate course material on an independent basis under the guidance of a faculty member.

This course is designed for the student who wants to be optimally prepared to perform quantitative analysis at a level consistent with (and exceeding) expectations for MBA interns in positions where quantitative sophistication is required. Its only prerequisite is the first-year Decision Analysis course; no additional quantitative experience or acumen is required. The course will focus primarily on data analysis, used to both gain useful insights into relationships and make better, more useful forecasts. In addition to more advanced treatment of regression analysis (the goal being for students to be able to build and apply sophisticated regression models), students will become familiar with other common approaches to forecasting, such as rudimentary time-series analysis. Students will also improve their ability to structure, analyze, and manage situations involving uncertainty and risk, using simulation (Crystal Ball), decision trees, and the other tools introduced in the required Decision Analysis course. Finally, the course will introduce students to the concepts of optimization using Excel's Solver add-in, used to determine how to optimally allocate resources in situations involving complex trade-offs.

Formal record of student commitment to project research for Master of Engineering degree under the guidance of a faculty advisor. Registration may be repeated as necessary.

Engages students in identification of a research question, a review of the literature and the application of an existing data science tool or technique (algorithm) to that problem. This is a mentored experience and will allow the student to demonstrate their capacity for research and begin to develop a relationship with a faculty mentor in Data Science. Course requires instructor permission.

For doctoral students.

For doctoral students.