NUS's MSc in Data Science and Machine Learning, offered through NUS's top-ranked School of Computing, is one of the fastest-growing graduate programs at Asia's highest-ranked university (QS #8, THE #17, US News Global #20 for 2026). It draws heavily on Singapore's position as a regional data, fintech, and AI hub — DBS Bank, Temasek and GIC, and Singapore government agencies all recruit data-science talent directly from NUS, alongside major technology firms with a Singapore presence like Google, Meta, and ByteDance. This guide covers acceptance rate context, costs, admission requirements, and career outcomes specific to this program.
NUS MSc Data Science and Machine Learning Acceptance Rate
NUS does not publish an official aggregate acceptance rate for any program, including the MSc in Data Science and Machine Learning. This is consistent across NUS's admissions communications — the university does not release program-level or university-wide admit-rate figures.
| Metric | Figure | Source/Note |
|---|---|---|
| NUS official aggregate acceptance rate | Not published | NUS does not release this figure for any program |
| Third-party estimates (university-wide, all programs) | Roughly 5%–20% | Varies widely by methodology; unverified |
| MSc Data Science and Machine Learning-specific acceptance rate | Not published | As a newer, high-demand specialization within Computing, applicant volume is understood to be high relative to seats |
Honest read: As with the rest of NUS's programs, no verified acceptance-rate figure exists for this MSc specifically. Data science and machine learning master's programs globally have seen a sharp rise in applicant volume over the past several years as AI/ML hiring has grown, and NUS's program — backed by the School of Computing's Asia-leading rankings and Singapore's fintech/AI-hub status — is reasonably understood to be one of the more competitive programs within NUS's graduate Computing offerings, even without an official number to cite. Don't anchor your preparation on an unverified percentage; focus on building a genuinely strong quantitative and applied-ML profile instead.
NUS MSc Data Science and Machine Learning Fees and Cost of Attendance
NUS's dataset does not break out a distinct tuition figure specifically for the MSc in Data Science and Machine Learning separate from its general School of Computing (Computing/Engineering-tier) fee schedule, so the figures below use that representative tier — confirm the program's exact current tuition on NUS's official fee schedule, since specialized master's programs can sometimes carry their own rate distinct from the general Computing tier.
| Cost Item | Amount (USD/year, converted from SGD) |
|---|---|
| International tuition (Computing/Engineering-tier, non-subsidised, representative) | ~$28,770 (SGD 39,700) |
| Living expenses (estimated) | ~$18,840 |
| Total estimated cost of attendance | ~$47,610 |
| Application fee | $79 |
As with all NUS non-subsidised tuition figures, the MOE Tuition Grant can substantially reduce this cost in exchange for a bonded obligation to work for a Singapore-registered company for a fixed period after graduation — a career and financial commitment worth weighing carefully rather than treating as a simple discount.
Admission Requirements for NUS MSc Data Science and Machine Learning
| Requirement | Detail |
|---|---|
| Minimum academic bar | No single GPA scale for international applicants; most taught master's programs (including this MSc) expect the equivalent of a good Honours degree (upper-second class or higher), typically in computer science, statistics, mathematics, engineering, or a closely related quantitative field |
| GRE/GMAT | Not typically required for this MSc; some NUS Business School-adjacent programs require it, but this is not standard for Computing/data-science tracks |
| Application fee | $79 |
| Intake | Semester 1 (August); rolling, program-specific deadlines |
Documents required:
- Direct online application via NUS's graduate admissions portal (no centralized application system)
- Official transcripts from all previously attended institutions
- Statement of purpose (program-dependent)
- CV/resume
- Academic and/or professional references
- English language proficiency test scores, unless exempt
Given the quantitative intensity of data science and machine learning coursework, competitive applicants typically bring a strong mathematics/statistics foundation (linear algebra, probability, and statistical inference) alongside programming proficiency, and — increasingly, given the field's growth — some demonstrable applied ML experience (research, an industry project, or a strong portfolio) beyond coursework alone.
How to Get Into NUS's MSc Data Science and Machine Learning Program
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Show quantitative depth, not just coding ability. Data science and ML admissions committees look past general programming skill toward genuine statistical and mathematical foundation — make your linear algebra, probability, and statistics coursework and any applied project work explicit in your application.
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Apply directly through NUS's own portal and check your country's equivalency table. NUS does not use a centralized application system; international applicants are assessed against NUS's own published country-specific equivalency tables for prior qualifications — read the guidance for your specific degree system carefully.
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Connect your goals to Singapore's data/fintech ecosystem where genuine. DBS Bank, Temasek and GIC, and Singapore government agencies are significant employers of data-science talent regionally; if your career interests align with fintech, quant finance, or public-sector data work, make that explicit rather than presenting a generic "I like machine learning" narrative.
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Differentiate through a portfolio or research angle. Given rising global demand for data science/ML master's programs, applicants with a specific research interest, a strong project portfolio (Kaggle competitions, open-source ML contributions, or a substantial capstone project), or relevant industry experience will stand out more than a coursework-only profile.
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Recommended prep timeline: NUS's own guidance suggests around 7 weeks of focused English-test preparation for most graduate programs, given NUS's comparatively moderate standard requirement — but treat this as a floor, not a ceiling, particularly if your quantitative portfolio needs more development time than your English-test prep.
English Proficiency Requirement (IELTS/TOEFL) for NUS MSc Data Science and Machine Learning
NUS's graduate English requirement broadly mirrors its undergraduate standard, though some programs may set their own threshold — always confirm on the specific program's admissions page.
| Test | Minimum |
|---|---|
| IELTS Academic | 6.5 overall |
| TOEFL iBT | 92 overall |
| PTE Academic | Accepted (minimum not published) |
| Cambridge English C1 Advanced | Accepted |
For a full breakdown by faculty, competitive benchmarks, and preparation strategy, see Gabble's dedicated guides: NUS IELTS requirements and NUS TOEFL requirements.
Applicants who completed a prior degree taught and examined entirely in English at a recognized institution, or who completed secondary/tertiary education in an English-medium system, are typically exempt from submitting a separate test score. Score validity is 2 years.
Career Outcomes After NUS MSc Data Science and Machine Learning
NUS does not publish a program-specific employment rate or average starting salary for this MSc, but the university's graduate-level top recruiters map closely onto data-science and ML career paths, given the heavy presence of banking, consulting, sovereign-wealth investment, and government analytics roles in Singapore.
Top recruiters (NUS graduate level, university-wide):
- McKinsey & Company
- Boston Consulting Group
- DBS Bank
- Temasek Holdings and GIC
- Singapore Government agencies
- International banks with a Singapore regional headquarters
Graduates of this MSc are particularly well-positioned for quantitative and data-science roles at DBS Bank and other regional banks, analytics and modeling roles at Temasek and GIC (among the world's largest sovereign wealth funds), and data/ML engineering roles at Singapore-based offices of major technology firms like Google, Meta, and ByteDance — alongside a growing base of data-driven public-sector roles across Singapore's government agencies.
FAQ
Does NUS publish an acceptance rate for the MSc in Data Science and Machine Learning? No. NUS does not publish an official aggregate acceptance rate for any program. Third-party estimates for NUS overall range widely (roughly 5%–20%) depending on methodology, but no verified figure exists for this MSc specifically.
Is the GRE or GMAT required for this MSc? Generally no — this program does not typically require GRE or GMAT scores, unlike some NUS Business School MBA-adjacent programs. Confirm on the program's own admissions page, since policy can change.
What academic background do I need for NUS's Data Science and Machine Learning MSc? A good Honours degree (upper-second class or higher) in computer science, statistics, mathematics, engineering, or a closely related quantitative field is typical, along with a solid mathematical foundation in linear algebra, probability, and statistics.
What IELTS or TOEFL score do I need for this program? The standard NUS graduate requirement is IELTS 6.5 overall or TOEFL iBT 92 overall. See Gabble's NUS IELTS requirements and NUS TOEFL requirements guides for the full breakdown.
How much does NUS's Data Science and Machine Learning MSc cost? Using NUS's representative Computing-tier tuition, international non-subsidised tuition is roughly $28,770/year (SGD 39,700), with an estimated total cost of attendance around $47,610/year — confirm the program's exact figure on NUS's official fee schedule, and note that the MOE Tuition Grant can significantly reduce cost in exchange for a post-graduation work-bond obligation.
Which companies hire NUS data science and ML graduates? DBS Bank, Temasek Holdings and GIC, McKinsey, Boston Consulting Group, Singapore government agencies, and international banks with regional Singapore headquarters are among the top recruiters, alongside strong demand from Google, Meta, and ByteDance's Singapore offices.