John Hopkins University

What you'll study

The programme combines rigorous foundations with applied and systems-oriented topics. Typical modules cover:

• Algorithms and theory — advanced algorithm design and analysis, complexity.
• Operating systems and distributed systems — concurrency, resource management and large-scale system design.
• Computer networks and cloud computing — protocols, networked services, cloud architectures and deployment.
• Databases and data management — database systems, big data platforms and data engineering.
• Cybersecurity and privacy — applied security, cryptography fundamentals and secure system design.
• Machine learning and data science — statistical learning methods, applied ML systems for IT tasks.
• Software engineering and project work — large-scale software development, testing, and team-based capstone projects.
• Electives and interdisciplinary options — human–computer interaction, bioinformatics, financial computing, applied mathematics and courses offered across Johns Hopkins’ engineering, public health and business units.

Students typically choose between a thesis or a substantial applied project/capstone. The programme is offered in full-time and part-time formats, including pathways designed for working professionals.

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree in computer science, engineering, mathematics, physics or a closely related quantitative discipline. Typical application components include:

• Academic transcripts demonstrating strong performance in core quantitative and computing courses.
• Statement of purpose that outlines motivations, relevant experience and study or career goals.
• Letters of recommendation (usually two or three) from academic instructors or professional supervisors who can attest to technical ability and potential for graduate study.
• Curriculum vitae or résumé describing relevant projects, internships and work experience.
• Standardised tests — requirements vary by intake and programme route; some applicants may submit GRE scores where requested. International applicants must demonstrate English proficiency through accepted tests unless otherwise exempt.

Admissions decisions take into account academic preparation, relevant experience, research or project potential, and fit with available faculty supervision for research-focused tracks.

Career prospects

Graduates enter a wide range of roles across technology and IT-driven organisations. Common career paths include:

• Software engineer, backend or full-stack developer
• Systems architect and cloud infrastructure engineer
• Data engineer and machine learning engineer
• Cybersecurity analyst, security engineer or information assurance specialist
• Site reliability engineer (SRE) and DevOps engineer
• Technical product manager or engineering manager
• Research scientist or PhD candidate in computer science or related disciplines

Johns Hopkins’ strong connections with industry, research labs and government agencies support internships and employer recruitment, helping graduates move into senior technical roles, consultancy and leadership positions in both private and public sectors.

Why study at John Hopkins University

Johns Hopkins combines a research-intensive environment with applied, multidisciplinary collaborations. The university’s Department of Computer Science is active in systems, security, machine learning and computational methods, and students can draw on resources across engineering, public health, medicine and business to pursue interdisciplinary projects.

• Access to leading faculty and research groups working on practical problems in networks, security, data systems and AI.
• Opportunities to work with large-scale facilities and centres, including collaborations with the Applied Physics Laboratory and local industry partners.
• A strong alumni and employer network in technology, healthcare, finance and government sectors, supporting placements and career progression.

The programme is suitable for those who want a balance of theoretical depth and hands-on systems experience, preparing graduates for technical leadership, advanced development roles or further research studies.