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| Master of Science in Computer Science | |
|---|---|
| Name | Master of Science in Computer Science |
| Abbreviation | MSCS, MSc CS |
| Type | Graduate degree |
| Typical duration | 1–3 years |
| Prerequisite | Bachelor's degree in related field |
| Focus | Advanced computing, algorithms, systems, theory |
Master of Science in Computer Science A Master of Science in Computer Science is an advanced graduate degree awarded by universities to students who complete postgraduate study in computing. Programs emphasize advanced Turing-era theory, Lovelace-inspired programming practice and applications across domains linked to institutions like Massachusetts Institute of Technology, Stanford University, University of Oxford, University of Cambridge and Carnegie Mellon University. Curricula often bridge theoretical foundations associated with Church and von Neumann and applied strands tied to industry partners such as Google, Microsoft, IBM, Intel, and Amazon.
A graduate degree in computing builds on undergraduate study from universities such as Harvard University, Princeton University, California Institute of Technology, University of California, Berkeley, ETH Zurich and Tsinghua University. Typical content includes coursework in areas related to Knuth’s algorithm analysis, Dijkstra’s software engineering principles, Dijkstra-linked programming languages, and system designs inspired by Unix pioneers and companies like Bell Labs, Bell Labs and AT&T. Programs often reference standards and frameworks from bodies such as IEEE and ACM and collaborate with research centers like Microsoft Research and Google DeepMind.
Admission requirements commonly cite undergraduate degrees from institutions such as University of Toronto, University of Illinois Urbana-Champaign, Peking University, National University of Singapore, and Imperial College London. Applicants may need transcripts referencing coursework tied to figures like Shannon and Backus, letters from faculty at places like Columbia University or University of Michigan, and standardized test scores that historically included GRE; some schools now waive GRE requirements. Prerequisites often include exposure to work influenced by Wirth and Liskov with foundational courses analogous to offerings at Cornell University or Technical University of Munich.
Core curricula cover subjects originating from theorists such as Cook and Karp and practical areas adopted by labs at Bell Labs, Xerox PARC, RPI and University of Waterloo. Common specializations include: - Artificial intelligence and machine learning tracing roots to Minsky, McCarthy, Hinton, Bengio, and LeCun. - Computer systems and operating systems reflecting lineage from Thompson, Ritchie, and Tanenbaum. - Cybersecurity drawing on research from Diffie and Hellman. - Human–computer interaction with influences from Norman and Shneiderman. - Data science and databases connected to work by Gray and Stonebraker. - Theoretical computer science and algorithms influenced by Turing, Church, Lamport and Dijkstra.
Courses often mirror syllabi from Stanford University, MIT, UC Berkeley, ETH Zurich, National Taiwan University and Seoul National University and adopt software toolchains used at Google, Facebook, NVIDIA and OpenAI.
Research pathways follow models established at Carnegie Mellon University, MIT, University of California, Berkeley, Oxford, and Cambridge with thesis supervision by faculty associated with labs like Microsoft Research, Google Research, FAIR and DeepMind. Thesis topics often engage with problems studied by researchers connected to LeCun, Fei-Fei Li, Ng, Koller and Schmidhuber. Capstone projects emulate industry collaborations with partners such as Tesla, Alibaba, Baidu, Huawei and Salesforce; some programs require publications in conferences like NeurIPS, ICML, CVPR, SIGCOMM, PLDI and POPL.
Program lengths vary from one-year intensive tracks at schools like University of Oxford and Imperial College London to two-year models at University of Toronto and three-year part-time options offered by LSE-partner institutions. Delivery includes on-campus instruction at institutions such as Harvard, blended models practiced at University of Edinburgh and online degrees from providers associated with Coursera partners and universities like Georgia Institute of Technology and University of Illinois Urbana-Champaign. Accreditation may reference national agencies recognized by governments and professional bodies including ABET and standards cited by EHEA-aligned universities.
Graduates pursue roles at companies like Google, Apple, Microsoft, AWS, Meta, Palantir and startups spun out of Y Combinator. Typical positions include software engineer, research scientist, systems architect, data scientist and security analyst. Alumni may continue to doctoral study at places including Stanford, MIT, Princeton, ETH Zurich, University of Cambridge and Caltech; some follow career paths through research institutions like Bell Labs or government labs such as Los Alamos National Laboratory and Lawrence Berkeley National Laboratory.
Regional differences appear between North American research-led programs at MIT, Stanford and Carnegie Mellon, European taught-master models at University of Cambridge, University of Oxford, ETH Zurich and ENS, and Asian offerings at Tsinghua University, Peking University, University of Tokyo, National University of Singapore and Seoul National University. Notable specialized programs include industry-aligned masters at Georgia Tech (online), research-heavy tracks at University of California, Berkeley and joint initiatives between corporate labs and universities such as collaborations involving Microsoft Research and IBM Research.
Category:Computer science degrees