John Hopkins University

What you'll study

The programme combines core computer science foundations with elective specialisations in areas such as machine learning, data science, cybersecurity, human–computer interaction and distributed systems. Students follow a mix of required core modules and elective courses, and can choose between a coursework‑focused track with a capstone or project, and a research/thesis track.

• Core topics: algorithms and complexity, advanced programming and software engineering, operating systems, databases, and computer networks.
• Common electives and specialisations: machine learning and artificial intelligence, statistical learning and data mining, big data systems, cloud and distributed computing, computer security and privacy, natural language processing, human‑computer interaction, bioinformatics and health informatics.
• Practical experience: project courses, group capstone projects with industry or campus partners, internships, and opportunities to work with faculty on research in laboratories across the university.
• Research options: students pursuing the thesis track undertake supervised research leading to a master’s thesis and may prepare for continued study at PhD level.

Entry requirements

Applicants normally hold a bachelor’s degree in computer science, information science, engineering, mathematics, statistics or a closely related discipline. Competitive applicants demonstrate a strong quantitative and programming background, evidenced by undergraduate coursework or professional experience.

• Official transcripts from all post‑secondary institutions attended.
• A current CV or résumé outlining relevant academic and professional experience.
• Letters of recommendation, typically two to three, from academic or professional referees who can attest to technical ability and readiness for graduate study.
• A personal statement describing academic interests, specific areas of focus within computer and information sciences, and professional or research goals.
• Standardised tests: requirements vary by department and applicant background; some applicants may be asked to submit GRE scores while other applicants may be evaluated without them. International applicants must provide evidence of English language proficiency if required by the university.

Relevant professional experience, strong programming samples or prior research publications can strengthen an application. Meeting minimum entry expectations does not guarantee admission; selection is competitive and considers the overall profile.

Career prospects

Graduates move into technical and leadership roles across technology, finance, healthcare, government and research organisations. Typical job titles include:

• Software engineer / software developer
• Data scientist or data engineer
• Machine learning engineer or AI specialist
• Systems architect or cloud solutions engineer
• Cybersecurity analyst or information security engineer
• Research engineer or research scientist (industrial or academic)

Given Johns Hopkins’ strengths in medicine, public health and engineering, graduates also find specialised roles in health informatics, computational biology and industry collaborations with the Applied Physics Laboratory and other research centres. Many alumni continue to doctoral programmes or move into product leadership and technical management roles.

Why study at Johns Hopkins University

Johns Hopkins offers a strong research environment with faculty who are leaders in computing and interdisciplinary applications. The university’s infrastructure — including well‑resourced labs and links with the Applied Physics Laboratory — provides students with opportunities for hands‑on research and industry‑relevant projects.

• Interdisciplinary collaboration: easy access to collaborators in medicine, public health, engineering and the social sciences for cross‑cutting projects.
• Research and industry connections: partnerships with research centres, local technology firms and federal agencies that support internships and project placements.
• Faculty and mentorship: faculty with active research programmes in AI, security, data science and systems, plus opportunities for close mentorship on research projects and theses.
• Career support: university career services, networking events and an international alumni community that help students transition into industry or further study.