Graduate Certificate in Systems Engineering
This certificate program is designed to address industry's increased needs for engineers who have expertise in Systems Engineering. It will prepare today's engineers to be competitive in taking on the new challenges facing the industry so that our companies can compete globally.
The certificate is a Purdue University certificate that would appear on a student’s transcript upon completion.
Who should join the program?
Practicing engineers who joined the workforce after bachelor’s degree, newly graduated engineers, and existing graduate students would be interested in obtaining training in this area in order for them to be current in solving demanding systems engineering problems. The proposed certificate program will provide them with the required technical skills.
Requirements to complete the graduate certificate program
- Total requirement: 12 credit hours
- Minimum overall GPA: Successful completion of the certificate requires at least a B average over all courses counting toward the certificate.
- Minimum grade: Courses with a grade of C- or less must be taken again to count towards the certificate. The minimum grade that will be accepted in any single course is C. For transfer credits, only the courses taken that result in a grade of B or better may be transferred for this certificate program.
There are two courses in the core area, three courses in the primary area, and a number of courses in the related area. The certificate requires completion of two courses in the core area, at least one course in the primary area, and any remaining course in the related area.
Take the core area courses, including:
- ME 53501 Introduction to Systems Engineering Principles
- ME 53502 Systems and Specialty Engineering
Take at least one course in the primary area from the following list:
- ME53504 Systems Driven Product Development
- ME53503 Model Based Systems Engineering
- ME57101 Probabilistic Engineering Design
Take at most one course from the following list of the related area:
- ME 57500 Theory and Design of Control Systems
- ME 58100 Numerical Methods in Mechanical Engineering
- ME 59100 Mechanical Engineering Projects I
- ME 50601 Design Optimization Methods
- ECE 57000 Artificial Intelligence
- ECE 56500 Computer Architecture
- ECE 58000 Optimization Methods for Systems and Control
- ECE 60200 Lumped System Theory
- ECE 68000 Modern Automatic Control
- STAT 51100 Statistical Methods I
- STAT 51200 Applied Regression Analysis
- STAT 51400 Designs of Experiments
- PBHL B561 Introduction to Biostatistics I
Are there on-line options for these courses?
Yes. The majority of the graduate courses are offered in late afternoon hours to accommodate the needs of part-time students. In addition, the first two required courses (ME 53501 and ME 53502) may be available in both live lecture and online via video streaming modes.
Will any of these four courses count toward a graduate degree?
Yes! All four courses may be used toward the requirements for a Master of Science in Engineering Degree, if one wishes to pursue a formal degree program.
What are the requirements for admission to the certificate program?
In order to be eligible for this certificate program, the students must have a bachelor's degree from an accredited institution in an area, which provides the necessary mathematical preparation for an engineering degree with a minimum undergraduate GPA of 3.0 out of 4.0. A conditional admission may be offered for applicants not meeting this criterion who have superior overall credentials. Applicants with non-engineering degrees, including mathematics, physical sciences, and engineering technology, may be required to take undergraduate mechanical engineering courses before admission to the program. Appropriate work experience also will be taken into account in making decisions about admission. Students will be required to submit a statement of interest and three letters of recommendation. A minimum TOEFL score of 80 (internet based) or higher is required for international applicants whose native language is not English. Applicants taking IELTS must score at least 6.5 on the academic module.
Students admitted directly to the Purdue University graduate program can be considered for this certificate program, provided the student formally applies for the certificate program and receives admission. Courses completed under the certificate program are not automatically transferred to a graduate degree program unless the student makes a petition to the graduate committee in respective departments. A student already enrolled in a graduate degree program may complete the certificate irrespective of his/her major so long as the requirements of the certificate are fulfilled.
I have completed a few graduate courses in the past. Can I use the credits toward the certificate program?
If you have already earned credits for one or more of the equivalent courses prior to admission, you may request to transfer up to a maximum of 6 credits of these courses toward this certificate. The rest of the courses must be completed at IUPUI within a three-year period from the time of admission. Any waivers or substitutions require approval. No undergraduate courses can be applied to this certificate program.
How do I apply for admission to the certificate program?
To apply for admission, contact Monica Stahlhut, MEE Graduate Coordinator by email: firstname.lastname@example.org
Program Course Listing and Descriptions
ME 53501 Introduction to Systems Engineering Principles (3 cr.) This course offers an examination of the principles of systems engineering and their application across the system life cycle. Special emphasis is given to concept exploration, requirements analysis and development, analysis of alternatives, preliminary design, integration, verification, and system validation. The students will use the international space station (on-orbit modules) for practical application of the principles introduced in this course. This is the first of two courses in systems engineering and is a prerequisite to the Systems & Specialty Engineering course. Both courses use the same textbook and have a 15% overlap of the text material.
ME 53502 Systems and Specialty Engineering (3 cr.) This course offers an examination of the interaction between the principles of systems engineering and the “design for” specialty engineering areas. The focus of their interactions is viewed across the system life cycle. Special emphasis is given to contributions of the specialties to the essential knowledge development needed for concept exploration, requirements analysis and development, trade-offs and decisions with uncertainty, preliminary design, system integration, verification, and system validation. This is the second of two courses in systems engineering and is dependent upon successfully completing ME 53501 Introduction to Systems Engineering Principles.
ME53504 Systems Driven Product Development (3 cr.) Integrated Model-based systems driven product development, or SDPD (Systems Driven Product Development) is a framework for integrating system behavioral modeling with downstream design and manufacturing practices. SDPD is an implementation of MBE (Model-based Engineering) which integrates MBSE (Model-based Systems Engineering) and PLM (Product Lifecycle Management). SDPD can also be seen as an approach to creating the “Digital Twin” of a product/process, and supporting the digital factory/digital enterprise of Industry 4.0 (4th Industrial Revolution). In addition to introducing key concepts and definitions such as MBSE (Model-based Systems Engineering), SDPD, Digital manufacturing, Digital Twin, Digital Thread, Industry 4.0, Interoperability, Traceability, Validation/Verification, Predictive analytics, etc., the course will focus on covering the key tools that enable the implementation and demonstration of System driven model-based integrated product and process lifecycle, including: Cameo (for MBSE), Amesim (for Systems simulation), NX CAD (for 3D modeling), Star-CCM+ and NASTRAN (for model analysis), Teamcenter Process planner (for process design), Tecnomatix (for process and plant simulation), HEEDS (for design optimization), and Teamcenter (for Product data and lifecycle management). The course includes at least one case study and one project that leverage these technologies to implement SDPD as a key step towards building the digital solution that drives Industry 4.0.
ME53503 Model Based Systems Engineering (3 cr.) MBSE (Model Based Systems Engineering) is the “formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning at the conceptual stage and continuing to design, design analysis and throughout the product life cycle stages” (INCOSE). The course covers the fundamental concept behind MBSE and introduces SysML for modeling systems using a computer based tool, the Cameo Systems Modeler.
- SysML more specifically is a general-purpose modeling language for systems engineering. The coursework includes the SysML semantics and the syntax to represent engineering requirements, structure, behavior, allocations, and constraints on system properties to support engineering analysis.
- The course work covers MagicGrid and OOSEM methodology to analyze and define system requirements, system architecture, interfaces, use cases, operations (system behaviors), and more.
- The class includes computer lab tutorials on Cameo Systems Modeler Tool complementing the coursework. You will learn to model systems using to model case studies using OMG SysML.
ME57101 Probabilistic Engineering Design (3 cr.) This course presents probabilistic methodologies of engineering design under uncertainty. It is intended for students who are interested in statistical/probabilistic methods for engineering analysis and design. The outcomes of the course are 1) an ability to model uncertainties in engineering applications, 2) an ability to perform basic statistics, risk, and reliability analyses, and 3) an ability to integrate probabilistic design with simulations, optimization, Design for Six Sigma, and Design of Experiments.
ME 57500 Theory and Design of Control Systems (3 cr.) Modern control techniques, state space representations, performance evaluation, controllability, observability, and observer design are introduced. The Bond graph is developed as a versatile computer-aided method of modeling coupled systems.
ME 58100 Numerical Methods in Mechanical Engineering (3 cr.) The solution to problems arising in mechanical engineering using numerical methods. Topics include nonlinear algebraic equations, sets of linear algebraic equations, eigenvalue problems, interpolation, curve fitting, ordinary differential equations, and partial differential equations. Applications include fluid mechanics, gas dynamics, heat and mass transfer, thermodynamics, vibrations, automatic control systems, kinematics, and design.
ME 50601 Design Optimization Methods (3 cr.) General theory of optimization, concepts and problem statement are presented. Methods for minimization of a function of one and multiple variables with and without constraints are covered along with response surface methods and design of experiments. A class project uses a commercial software package to solve typical engineering design optimization problems. In addition to various engineering disciplines, the methods studied can be applied to a variety of diverse disciplines including finance, life sciences and physics.
ME 59100 Mechanical Engineering Projects I (1-6 cr.) Sem. 1 and 2. Summer Session. Projects or special topics of contemporary importance or of special interest that are outside the scope of the standard graduate curriculum can be studied under the Mechanical Engineering Projects courses. Interested students should seek a faculty advisor by meeting with individual faculty members who work in their area of special interest and then prepare a brief description of the work to be undertaken in cooperation with the advisor.
ECE 57000 Artificial Intelligence (3 cr.) The course covers the application of Artificial Intelligence techniques and algorithms for problem solving. Students will learn to apply different machine learning approaches to solve real life problems related to searching, parsing, identification, prediction, clustering, feature selection, etc… The concepts of reasoning an inference will be discussed and used as an approach to reason about difficult problems and derive suitable algorithms for these problems.
ECE 56500 Computer Architecture (3 cr.) An introduction to problems of designing and analyzing current machine architectures. Major topics include performance and cost analysis, pipeline processing, vector machines and numerical applications, hierarchical memory design, and multiprocessor architectures. A qualitative approach allowing a computer system designer to determine the extent to which a design goal is emphasized.
ECE 58000 Optimization Methods for Systems and Control (3 cr.) Introduction to optimization theory and methods, with applications in systems and control. Nonlinear unconstrained optimization, linear programming, nonlinear constrained optimization, various algorithms and search methods for optimizations, and their analysis. Examples from various engineering applications are given.
ECE 60200 Lumped System Theory (3 cr.) An investigation of basic theory and techniques of modern system theory, emphasizing linear state model formulations of continuous- and discrete-time systems in the time and frequency domains. Coverage includes notion of linearity, time invariance, discrete- and continuous-times state models, canonical forms, associated transfer functions and impulse response models, the state transition matrix, the Jordan form, controllability, observability, and stability.
ECE 680 Modern Automatic Control (3 cr.) Theoretical methods in optimal control theory. Topics include the calculus of variations and the Pontryagin minimum principle with applications to minimum energy problems. Geometric methods will be applied to the solution of minimum time problems. Computational methods, singular problems, observer theory, and sufficient conditions for existence of solutions are also discussed.
STAT 51100 Statistical Methods I (3 cr.) Descriptive statistics; elementary probability; random variables and their distributions; expectation; normal, binomial, Poisson, and hypergeometric distributions; sampling distributions; estimation and testing of hypotheses; neway analysis of variance; correlation and regression.
STAT 51200 Applied Regression Analysis (3 cr.) Inference in simple and multiple linear regression, estimation of model parameters, testing and prediction. Residual analysis, diagnostics and remedial measures. Multicollinearity. Model building, stepwise and other model selection methods. Weighted least squares. Nonlinear regression. Models with qualitative independent variables. One-way analysis of variance. Orthogonal contrasts and multiple comparison tests. Use of existing statistical computing package.
STAT 51400 Designs of Experiments (3 cr.) Fundamentals, completely randomized design, randomized complete blocks. Latin squares, multiclassification, factorial, nested factorial, incomplete blocks, fractional replications, confounding, general mixed factorial, split- plot and optimum design. Use of existing statistical computing packages.
PBHL B561 Introduction to Biostatistics I (3 cr.) This course introduces the basic principles and methods of data analysis in public health biostatistics. Emphasis is placed on public health examples as they relate to concepts such as sampling, study design, descriptive statistics, probability, statistical distributions, estimation, hypothesis testing, Chi-Square tests, T-tests, Analysis of Variance, linear regression models, and correlation analyses. SAS software is required for some of the homework questions.