MSc in Software Engineering Systems in Boston United States | Northeastern University

The Master of Science (MS) in Software Engineering Systems (previously known as Computer Systems Engineering) takes an analytical, engineering-focused approach to software development. You'll develop sophisticated design capabilities, expand your expertise in cloud computing, and create machine learning algorithms. Driven by your enthusiasm for cutting-edge technology, large-scale machine learning, and AI-driven solutions, you'll transform into a skilled problem solver, proficient engineering architect, and outcome-oriented leader. Your understanding of the intersection between computer science, engineering principles, and ethical considerations will empower you to make tangible societal contributions in this rapidly growing field.
Northeastern provides flexible program options including coursework-only or thesis tracks, professional co-op opportunities, hands-on project work, and instruction from faculty at the forefront of industry advancements.
Our software engineering systems graduate program features extensive coursework in big-data engineering and analytics, complemented by specialized courses designed to effectively communicate data-driven insights to organizational leadership.
The curriculum encompasses data management systems, business intelligence solutions, columnar databases, data science methodologies, and large-scale data engineering. Students master advanced functional programming using Scala, explore cutting-edge data science techniques, and study cloud computing architectures. The program also includes multi-threaded concurrent computing - essential for coordinating massive server clusters performing parallel processing to achieve hundredfold performance gains in large-scale analytics. These complex domains demand the specialized mathematical capabilities unique to software engineers, who possess the skills to optimize algorithmic efficiency for superior results. Our graduates achieve data science fluency with a systems-building focus, learning to implement machine learning algorithms that extend beyond standard statistical packages.