Software Engineering student drawing on dry erase board
Master of
Software Engineering

Courses

The online Master of Software Engineering degree is a 36-credit program that includes a capstone course. The curriculum material addresses industry needs by teaching fundamental and theoretical concepts and includes intensive, comprehensive coverage of modern software concepts and techniques. The material emphasizes a holistic approach, encompassing financial, legal, and presales issues; technical concepts; software design techniques; methods; and project management.

The Master of Software Engineering (MSE) program is designed to help students with a technical background become leaders in the industry, while providing the convenience and flexibility of earning your degree online.

Build Your Professional Network

Penn State's online Master of Software Engineering is an ideal graduate degree program for students with undergraduate degrees in computer science, computer engineering, electrical engineering, or information sciences. The online courses are highly interactive and collaborative, allowing you to build strong ties with others and gain perspectives from other disciplines and industries.

Complete Your Engineering Degree in Two Years

The degree consists of 11 courses, which you will complete in continuous seven-week terms over two years. Your course work is designed so that you can continue to work full-time while earning your master's degree.

Maximum flexibility is maintained by the program in an effort to meet both the professional needs of individual students and academic quality standards.

Required Courses (36 credits)

The online software engineering degree program spans six continuous semesters with each semester containing two seven-week terms. The program is completed in two years by taking courses fall, spring, and summer semesters.

Year 1, Semester 1

  • 3
    credits

    Students will learn and practice the elements of constructing a large-scale distributed software system using current technologies.

  • 3
    credits

    Theory and applications of requirements elicitation, analysis, modeling, validation, testing, and writing for hardware and software systems.

Year 1, Semester 2

  • 3
    credits

    Software systems architecture; architectural design principles/patterns; documentation/evaluation of software architectures; reuse of architectural assets through frameworks/software product lines.

  • 3
    credits

    Best practices in the requirements, analysis, and design of large software systems including the Unified Modeling Language and the Unified Process.

Year 1, Semester 3

  • 3
    credits

    Analysis and construction of project plans for the development of complex software products; how to manage change and cost control.

  • 3
    credits

    This course provides a rigorous formal framework and practical information on the testing of software throughout its life cycle.

Year 2, Semester 4

  • 3
    credits

    The requirements capture, design, and development of relational database applications; analysis of business requirements and development of appropriate database systems.

  • 3
    credits

    This class examines well-known heuristics, principles, and patterns in the design and construction of reusable frameworks, packages, and components.

Year 2, Semester 5 (select 6 credits)

  • 3
    credits

    This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.

    • Prerequisite

      1 undergraduate course in probability or statistics.

    • Note

      Course available with permission.

  • 3
    credits

    This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation.

    • Prerequisite

      1 undergraduate course in probability or statistics.

    • Note

      Course available with permission.

  • 3
    credits

    Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large data sets.

    • Prerequisite

      STAT 500

  • 3
    credits

    Examination of large-scale data storage technologies including NoSQL database systems for loosely-structured data, and warehouses for dimensional data.

    • Prerequisite

      INSC 521

  • 3
    credits

    This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

  • 3
    credits

    This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

  • 3
    credits

    The course examines business intelligence in the era of big data. Emphasis is on the successful implementation of big data in large and small corporations that deliver extraordinary results.

  • 3
    credits

    Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

    • Prerequisite

      1 undergraduate course in probability or statistics.

    • Note

      Course available with permission.

  • 3
    credits

    This course will explore various emerging theories and technologies in information science, including at least cloud computing, mobile application, Internet of things, blockchain, machine learning, and artificial intelligence.

  • 3
    credits

    A web-centric look at the latest techniques and practices in computer security as they apply to the internet.

  • 3
    credits

    Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

    • Prerequisite

      INSC 521, or approval of instructor or department

Year 2, Semester 6

  • 6
    credits

    The 800-level studio provides an opportunity for students to undertake a substantial software project.

Course Availability

If you're ready to see when your courses will be offered, visit our public LionPATH course search (opens in new window) to start planning ahead.

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