This four-week online asynchronous bootcamp-style course prepares incoming students for the computational demands of the Systems Engineering program by building proficiency in modern data analysis and computing workflows. Students progress through weekly modules covering Python fundamentals and the data analysis stack, exploratory data analysis and visualization, and applied statistics for engineering data. Delivered entirely online, the course combines short video lectures with hands-on assignments, allowing students to work at their own pace while meeting weekly deadlines. By the start of the Fall semester, participants will have a working command of the computational tools and data analysis practices expected throughout the Systems Engineering curriculum.   Students who have completed undergraduate courses in Python program and probability & statistics may complete a proficiency test to waive this course.

This course introduces the foundational principles and practices of modern systems engineering as applied to the design, development, and management of complex engineered systems. Students examine the full system life cycle from stakeholder needs analysis and requirements engineering through architecture definition, design integration, verification and validation, and sustainment, drawing on established frameworks from INCOSE, NASA and other organizations.   Core topics include systems thinking, functional and physical decomposition, requirements analysis, interface management, trade studies and decision analysis, risk and configuration management, system modeling & analysis, and the role of model-based systems engineering (MBSE) in contemporary engineering. Through case studies and team-based projects students develop the mindset and skillset needed to define needs and requirements, balance competing constraints, and make system-level design decisions across engineering disciplines, preparing them for professional practice in systems engineering.

This course develops the modeling and analytical skills needed to represent and evaluate complex engineered systems across their life cycle. Students learn to construct and interpret a range of system models including functional, behavioral, structural, and parametric representations, using model-based systems engineering (MBSE) methods within industry-standard tooling. The course focus spans architecture modeling and requirements traceability, analytical modeling for performance and trade-space exploration, and computational techniques such as discrete-event simulation, Monte Carlo analysis, sensitivity and uncertainty quantification, and multi-criteria decision analysis. Emphasis is placed on selecting appropriate system abstractions, integrating models across disciplines, and using analysis methods to support architecture decisions, risk assessment, and verification planning. Through hands-on assignments and a project applying MBSE and analytical methods to a real-world system, students gain practical experience in using models to drive engineering decisions in modern systems practice.

The capstone project provides a culminating hands-on experience in which students apply the full body of systems engineering knowledge and methods developed throughout the program to a substantive, real-world system design project. Working in multidisciplinary teams—often in partnership with industry, government, or research sponsors—students take a complex problem from stakeholder engagement and needs analysis through requirements definition, architecture and design, modeling and analysis, verification planning, and risk and project management, producing a coherent system solution and supporting technical artifacts. Teams are expected to integrate model-based systems engineering practices, quantitative trade studies, and lifecycle considerations while managing schedule, scope, and team dynamics under realistic constraints. Deliverables typically include a system requirements document, architecture and design models, analysis and trade-study results, a verification and validation plan, and a final technical report and presentation to faculty and sponsors, demonstrating each student’s ability to lead and contribute to systems engineering efforts at a professional level.

This first-semester course establishes a shared engineering foundation across the cyber, mechanical, and electrical domains that systems engineers must integrate in modern systems. Students explore the core principles of mechanical systems (statics, dynamics, and basic machine elements), electrical and electronic systems (circuits, signals, sensors, and actuators), and cyber systems (embedded computing, microcontrollers, communication protocols, and basic software control), with an emphasis on how these disciplines interact within cyber-physical and mechatronic systems. Concepts are reinforced through hands-on laboratory and project work in which student teams design, build, and program mechatronic devices to meet defined performance requirements. By the end of the course, students will be conversant in the vocabulary and design trade-offs of each domain and prepared to work across disciplinary boundaries.

Students may select from course options to complete the Space Systems or the Cyber Systems track, or may design their own path and select from approved technical electives in Systems Engineering or any of the other Institute for Enterprise Engineering master’s programs (Cybersecurity, AI for Product Innovation, Master of Engineering Management etc) or departmental master’s programs.   Approved technical elective options include but are not limited to:

• Mission-Critical Autonomous Systems Engineering (SYSENG 590-01)
• Space Mission Engineering (SYSENG 590-02)
• Satellite Systems (SYSENG 590-03)
• Space Cybersecurity (SYSENG 590-04)
• Space Systems Design (ME 590)
• Optimization in Practice (AIPI 530)
• Explainable AI (AIPI 590)
• AI & the Physical World (AIPI 590)
• Fundamentals of AI in Cybersecurity (CYBERSEC 510)
• AI in Modern Security Operations (CYBERSEC 511)
• Cloud Cybersecurity & Operations (CYBERSEC 590)
• UX & Front-End Engineering (DESIGNTK 590)
• Human-Centered Computing (ECE 653)
• Full-Stack IOT Systems (ECE 655L)
• Edge Computing (ECE 654)
• Software Quality Management (MEMP 575)
• Software Engineering Management (MEMP 590)
• Design for Manufacturability (SYSENG 590-05)

The purpose of this course is to empower students to become collaborative, ethical leaders in the globalized, 21st-century workplace. Students learn concepts and practice skills that will enable them to transition from being an engineering sole contributor to managing and leading others as a business professional. Students gain a sound understanding of management and leadership; increase awareness of their own management and leadership styles; build and practice competencies essential for team success (e.g., effective communication, collaboration, conflict resolution); and become ethical leaders above reproach. Emphasis is on leading teams in a volatile, complex and interdependent world.

This comprehensive course examines core and evolving concepts in the business fundamentals of successful technology-based companies including Business Plan Development & Strategies, Marketing, Product & Process Development processes, Intellectual Property, Accounting, Finance, and Operations. Students will learn the fundamentals essential to understanding all aspects of a business and will be able to converse in some depth in each of the areas studied upon completion. Other topics will include Supply Chain Management, Stage-Gate Development Cycles, Balanced Scorecards, Blue Ocean Strategy, and Disruptive Technologies.

This course provides an in-depth perspective on model-based systems engineering (MBSE) as a disciplined approach to specifying, analyzing, designing, and verifying complex systems through formal, integrated models rather than document-centric artifacts. Students develop fluency in the Systems Modeling Language (SysML) and apply established MBSE methodologies within industry-standard modeling tools to build traceable system models across the life cycle. Topics include model organization and governance, requirements modeling and traceability, architecture definition, behavioral and interface modeling, parametric analysis, model verification and validation, and integration of MBSE with simulation and digital engineering environments. Through progressive modeling assignments and a project in which teams develop a substantive system model for a real-world system, students gain the practical skills to lead MBSE efforts and leverage models as authoritative decision-making artifacts in contemporary systems engineering practice.

This advanced course delves into the critical methodologies and practices of testing systems for safety and reliability. It equips students with the knowledge and skills to design, conduct, and analyze rigorous testing programs that ensure the safety and reliability of complex systems. By the end of the course, students will be able to:

• Apply various testing methodologies tailored for safety and reliability.
• Design and implement effective test plans and procedures.
• Analyze and interpret test data to identify and mitigate risks.
• Integrate testing activities seamlessly into the project lifecycle.

This course will equip students to control complex systems and orchestrate optimal performance, providing them with mastery of fundamental principles and techniques on topics such as feedback loops, analyzing intricate dynamics, and designing sophisticated control systems for stability, precision, and efficiency in dynamic and complex systems. By the end of the course, students will be able to:

• Demonstrate a firm grasp of classic and modern control techniques
• Leverage modern software tools to develop control systems effectively
• Implement control systems integration across complex systems
• Apply advanced methods like optimal and adaptive control

Projects are one of the key mechanisms for achieving organizational goals and implementing change, whether it is the design and launch of a new product, the construction of a new building, or the development of a new information system. This course will focus on defining project scope, developing project plans, managing project execution, validating project performance and ensuring project control. Additional topics covered include decision making, project finance, project portfolio selection, and risk management.

This course addresses the systems engineering methods required to develop autonomous systems that must perform reliably in mission-critical domains such as space and defense. Students examine the architecture of modern autonomous systems including perception, localization, planning, decision-making, and control stacks and the challenges created through the integration of machine learning components, sensor fusion, and human-machine teaming under strict reliability, security and mission-assurance constraints. Topics covered in the course include requirements and concept-of-operations development for autonomous systems, verification and validation of learning-enabled and non-deterministic components, runtime monitoring, fault tolerance and resilience, cybersecurity, and the ethical and regulatory frameworks shaping autonomous system deployment. Through hands-on lab work, case studies of fielded systems, and a team project that applies systems engineering and assurance methods to a mission-critical autonomous system, students develop the capability to engineer autonomous systems that are trustworthy, certifiable, and mission-effective.

This course examines the methods and analytical tools that enable systems engineers to design systems for efficient, reliable, and cost-effective manufacture across their intended production volumes and life cycles. Students learn to integrate manufacturing considerations early in the design process, applying Design for Manufacturing and Assembly (DFMA) techniques addressing cost, quality, reliability, testability, supply chain, and sustainment. Core topics include material and process selection, tolerance analysis and geometric dimensioning and tolerancing (GD&T), producibility assessment, additive and subtractive manufacturing trade-offs, assembly and joining strategies, manufacturing readiness levels, and the role of digital manufacturing and model-based definition in connecting design to production. Students will develop the cross-disciplinary judgment to balance functional, cost, schedule, and producibility requirements and to lead design decisions that reduce risk and total cost of ownership in modern engineered systems.

This course focuses on the design and analysis of end-to-end space missions, treating the spacecraft, payload, launch, ground, and operations elements as an integrated mission system. Students learn the structured process of space mission engineering and how to go from mission objectives and concept of operations through requirements flow-down, architecture trades, and preliminary design, informed by current NASA, DoD, and commercial practices. Core topics include orbital mechanics and mission geometry, payload and instrument sizing, spacecraft bus subsystems, launch vehicle selection and integration, ground systems and mission operations, link and data budgets, mission cost and risk estimation, and verification and validation across the mission life cycle. Through trade studies, design exercises, and a team mission design project in which students develop a concept for a representative space mission, students build the skill set needed to lead architecture and systems engineering decisions for modern civil, defense, and commercial space programs.

This course focuses on satellites as complex engineered platforms operating within broader mission, business, and operational ecosystems. Students examine the satellite as an integrated system covering the spacecraft bus and its major subsystems along with payloads and bus-payload interfaces, while also addressing the wider context in which satellites are developed and sustained. Topics include mission and stakeholder needs, requirements and architecture trades across satellite classes, the space environment and its programmatic implications, assembly, integration, and test, launch and deployment, on-orbit operations and anomaly response, end-of-life and space sustainability considerations, and the regulatory, cost, schedule, risk, and supply chain factors that shape civil, defense, and commercial satellite programs.

This course addresses the cybersecurity challenges unique to space systems, equipping engineers to identify, analyze, and mitigate cyber risks across the full space mission architecture. Students examine the evolving threat landscape facing civil, defense, and commercial space systems, Core topics include space system threat modeling and risk assessment, secure system architecture and zero-trust principles applied to space, cryptography and secure command and telemetry, supply chain and software assurance, ground system and mission operations security, resilience against jamming, spoofing, and on-orbit interference, and incident response and recovery for space assets.