Brandeis University

What you'll study

The programme combines core courses in computing fundamentals with elective options that let you focus on areas such as machine learning, data science, cybersecurity, human–computer interaction and systems engineering. Typical core modules cover:

• Algorithms and Theory: design and analysis of algorithms, complexity, and optimisation techniques.
• Programming and Software Engineering: advanced programming, software design, testing and large-scale system development.
• Databases and Information Management: database systems, data modelling, and data engineering principles.
• Computer Systems and Networks: operating systems, distributed systems and network architectures.
• Machine Learning and Data Science: statistical learning, neural networks, and applied data analysis.
• Security and Privacy: principles of cybersecurity, cryptography basics and systems hardening.

Programme formats typically include a mix of coursework and a culminating experience. Students may complete a supervised research thesis, a capstone project with a practical or industry partner, or a practicum/internship. Electives and seminars allow interdisciplinary study and collaboration with faculty across computer science, data science, and other departments.

Entry requirements

Typical admission requirements include a recognised bachelor’s degree in computer science, information systems, engineering, mathematics or a closely related discipline. Applicants without a computing undergraduate degree may be considered if they demonstrate relevant coursework, professional experience or show readiness through bridging courses.

• Academic transcripts: official records demonstrating prior undergraduate performance.
• Letters of recommendation: usually two or three letters from academic or professional referees who can speak to technical and/or research potential.
• Statement of purpose: a personal statement outlining academic background, career goals and reasons for choosing the programme.
• CV or résumé: detailing relevant work experience, projects and skills.
• English language proficiency: required for applicants whose first language is not English (accepted tests include TOEFL or IELTS, or equivalent evidence of proficiency).

Standardised tests such as the GRE may be optional or considered on a case-by-case basis; applicants should consult the programme’s admissions guidance for current testing policies. Professional experience, coding samples, project portfolios or GitHub links can strengthen an application.

Career prospects

Graduates move into a variety of technical and leadership roles across industry, government and research. Common career paths include:

• Software engineer or developer: building applications, backend systems and cloud services.
• Data scientist or machine learning engineer: applying statistical modelling and ML to extract insights and build predictive systems.
• Systems engineer and network architect: designing and maintaining scalable, reliable infrastructure.
• Cybersecurity analyst or engineer: protecting systems and data, conducting risk assessments and incident response.
• Product or technical manager: combining technical background with product strategy and team leadership.
• Research or PhD study: for those pursuing advanced research careers in academia or industrial research labs.

Brandeis graduates benefit from the university’s career services and proximity to the Boston–Cambridge technology ecosystem, which supports internship placements and employer connections across startups, established tech firms and research organisations.

Why study at Brandeis University

Brandeis offers a mid-sized research university environment with close faculty interaction, enabling personalised mentorship and opportunities to take part in active research projects. The Department emphasises both rigorous theory and applied work, so students gain practical skills as well as a strong conceptual foundation.

• Faculty expertise: opportunities to work with faculty engaged in research across AI, data science, systems, security and human-centred computing.
• Interdisciplinary options: easy access to collaborate with other departments and centres, reflecting Brandeis’s broader strengths in social sciences and entrepreneurship.
• Location and industry links: close to Boston and its large technology and biotech sectors, providing internship and employment opportunities.
• Support services: dedicated career services, labs and computing facilities that support project-based learning and professional development.

Overall, the Master’s in Computer and Information Sciences at Brandeis is designed for students who want a balance of strong technical training, research exposure and real-world experience to advance technical careers or continue to doctoral study.