Science & Mathematics

Statistics

The science of collecting, analyzing, and interpreting data—essential for research, business intelligence, and policy-making.

Overview

Statistics is the science of learning from data. It provides the mathematical framework for collecting, organizing, analyzing, and interpreting numerical information—turning raw numbers into meaningful insights. In an era of big data, statistics has never been more important or more in demand.

While data science focuses on engineering pipelines and building predictive models, statistics emphasizes the rigorous mathematical theory behind inference: how to draw reliable conclusions from uncertain data, how to design experiments that yield valid results, and how to quantify risk and uncertainty. A statistics degree covers probability theory, regression analysis, hypothesis testing, Bayesian methods, and time series analysis, giving you a deep toolkit for reasoning under uncertainty.

Graduates work as biostatisticians designing clinical trials, actuaries pricing insurance risk, market researchers analyzing consumer behaviour, and policy analysts informing government decisions. If you love mathematical reasoning, enjoy finding patterns in data, and want a career that combines analytical rigour with real-world impact, statistics is an excellent choice.

Statistics has been transformed by computational methods, and the world’s top departments reflect this evolution. Stanford’s Department of Statistics pioneered modern statistical learning theory—faculty there developed foundational methods like the lasso, CART, and random forests that now underpin much of data science. UC Berkeley’s Department of Statistics is deeply integrated with its data science ecosystem, and the Berkeley Institute for Data Science fosters cross-disciplinary collaboration. Oxford’s Department of Statistics is a leader in Bayesian methodology and computational statistics, while ETH Zurich’s Seminar for Statistics combines mathematical rigour with applied research in biostatistics and machine learning. Cambridge’s Statistical Laboratory maintains a strong tradition in probability theory and mathematical statistics.

In Singapore

Singapore's data-driven economy creates strong demand for statisticians across government agencies, financial institutions, healthcare, and the tech sector.

Career Outcomes & Salary

What jobs can I get and how much will I earn?

Entry Level0–2 years

$60,000–$100,000 (US) / £30,000–£48,000 (UK) / A$55,000–$80,000 (AU)

StatisticianData ScientistBiostatisticianActuarial AnalystQuantitative Analyst
Top employers
GoogleMetaPfizerGSKOffice for National StatisticsFederal Reserveinsurance companiesconsulting firms
Mid Career3–8 years

$100,000–$200,000 (US) / £55,000–£110,000 (UK) / A$90,000–$160,000 (AU)

Senior StatisticianPrincipal Data ScientistBiostatistics ManagerActuary (Fellow)Quantitative Researcher
Senior10+ years

$160,000–$400,000+ (US, senior roles in pharma, finance, or tech)

Chief StatisticianProfessor of StatisticsVP of Data ScienceDirector of Biostatistics—PharmaPartner—Quant Research
Industries
Technology & Data SciencePharmaceuticals & Clinical TrialsFinance & InsuranceGovernment & Public StatisticsAcademia & ResearchConsultingHealthcare & Epidemiology
Demand Outlook

Very strong—statisticians are in high demand across virtually every industry. The American Statistical Association reports consistent employment growth. Biostatistics and clinical trials, tech company experimentation, and government statistics all face talent shortages.

What You'll Learn

Core topics and skills covered in this degree

Probability Theory
Statistical Inference & Hypothesis Testing
Regression Analysis & Modelling
Experimental Design
Bayesian Statistics
Time Series Analysis
Multivariate Analysis
Computational Statistics & Simulation

Is This Right For Me?

Honest self-assessment to help you decide

WorkloadHeavy—expect 16–25 hours per week outside lectures on problem sets (mathematical proofs), computing assignments, data analysis projects, and reading. The combination of theoretical rigour and practical computing makes the workload consistently demanding.
Math LevelHigh—calculus, linear algebra, real analysis, and probability theory are all core. University statistics is substantially more mathematical than most students expect. The theory courses are as demanding as pure mathematics.
CreativityMore structured than creative—statistical methods follow rigorous procedures, and correctness is paramount. There is creativity in choosing appropriate methods, designing studies, and interpreting results, but within mathematical constraints.
TeamworkMostly solo for theory and problem sets. Computing projects may involve pair work. Applied projects often involve teams. The intellectual core is individual mathematical reasoning.

You'll thrive if...

  • You enjoy mathematical reasoning and want to apply it to understanding patterns in real-world data
  • You find uncertainty fascinating rather than frustrating—statistics is the science of making rigorous decisions when things aren’t certain
  • You like the idea of your work directly informing decisions—clinical trials, business strategy, government policy, and scientific research all depend on statistical analysis
  • You want a career that’s both intellectually rigorous and practically valued across every industry
  • You’re comfortable with both abstract theory (proofs, measure theory) and hands-on data work (programming, data cleaning, visualization)

Might not be for you if...

  • You find probability and abstract mathematics unpleasant—statistics is a mathematical discipline and the theory is demanding
  • You want to work exclusively with data without understanding the mathematical foundations—statistics requires theory, not just tool proficiency
  • You prefer creative, open-ended work over methodical analysis—statistics involves systematic, rigorous procedures
  • You’re uncomfortable with programming—modern statistics is inseparable from computing (R, Python, SQL)
  • You want immediate, visible results—much of statistics involves careful, sometimes tedious work on data quality, model checking, and uncertainty assessment
WorkloadHeavy—expect 16–25 hours per week outside lectures on problem sets (mathematical proofs), computing assignments, data analysis projects, and reading. The combination of theoretical rigour and practical computing makes the workload consistently demanding.
Math IntensityHigh—calculus, linear algebra, real analysis, and probability theory are all core. University statistics is substantially more mathematical than most students expect. The theory courses are as demanding as pure mathematics.
Creativity vs StructureMore structured than creative—statistical methods follow rigorous procedures, and correctness is paramount. There is creativity in choosing appropriate methods, designing studies, and interpreting results, but within mathematical constraints.
Group vs SoloMostly solo for theory and problem sets. Computing projects may involve pair work. Applied projects often involve teams. The intellectual core is individual mathematical reasoning.

A Day in the Life

What a typical week actually looks like

A typical week in Year 2 of a statistics programme is a blend of mathematical theory and data-driven practice...

High School Preparation

What to study and do before university

Recommended
HL Mathematics: Analysis and ApproachesHL Physics or HL EconomicsHL Computer Science
Helpful
SL Further Mathematics (if available)HL Biology

Skills to Develop

  • Master probability and combinatorics
  • Learn R or Python for data analysis
  • Practice thinking about uncertainty and variability
  • Work with real datasets

Extracurriculars

  • Enter data analysis competitions on Kaggle
  • Participate in mathematics competitions (AMC, UKMT)
  • Create a data analysis project and share it publicly
  • Learn to use spreadsheets at an advanced level
  • Follow data journalism outlets (FiveThirtyEight, The Economist’s data team, Our World in Data)

QS World Ranking 2026

Statistics & Operational Research

#University
1🇺🇸Stanford University
2🇺🇸Massachusetts Institute of Technology (MIT)
3🇺🇸Harvard University
4🇺🇸University of California, Berkeley (UCB)
5🇬🇧University of Oxford

How This Compares to Similar Majors

Side-by-side with related fields

Getting In — Admissions Guide

How competitive is this major and how to stand out

Competitiveness: High

Statistics programmes at top universities are competitive...

What Strengthens Your Application

  1. 1Exceptional mathematics results
  2. 2Further Mathematics (A-Level) or equivalent
  3. 3Experience with statistical software
  4. 4Mathematics competition results
  5. 5Evidence of interest in how statistics applies to real problems

Common Mistakes to Avoid

  • Confusing statistics with data entry or basic spreadsheet work
  • Not recognizing the mathematical demands
  • Thinking statistics is just 'applied maths'

Interview & Admission Tests

Where interviews are conducted (Cambridge, Oxford), expect probability puzzles...

General Preparation

These recommendations cover general preparation across Singapore universities. Specific programme requirements may differ—detailed per-programme requirements coming soon.

IB Diploma

  • Mathematics AA HL (essential)
  • Physics or Economics HL (helpful for applied context)

A-Level

  • H2 Mathematics (essential)
  • H2 Further Mathematics (highly recommended)
  • H2 Economics (helpful)

AP

  • AP Statistics (essential)
  • AP Calculus BC (essential)
  • AP Computer Science A (helpful)

IGCSE

  • Additional Mathematics (essential)
  • Mathematics (A*/A)
  • Economics (helpful)

Skills & Aptitudes

Love of mathematical reasoningComfort with uncertainty and probabilityPrecision and rigorProgramming ability (R or Python)Clear communication of numerical findings

NUS IB / A-Level admission requirements:NUS Admissions

Where to Study in Singapore

NUS

Faculty of Science

BSc (Hons) in StatisticsDetails

Similar Majors

Considering this major beyond Singapore?

View the global university major guide →

Frequently Asked Questions

What do you study in Statistics?

Statistics is the science of learning from data. It provides the mathematical framework for collecting, organizing, analyzing, and interpreting numerical information—turning raw numbers into meaningful insights. In an era of big data, statistics has never been more important or more in demand.

What can you do after a Statistics degree?

Typical entry-level roles: Statistician, Data Scientist, Biostatistician, Actuarial Analyst, Quantitative Analyst (starting salary $60,000–$100,000 (US) / £30,000–£48,000 (UK) / A$55,000–$80,000 (AU)). Key industries: Technology & Data Science, Pharmaceuticals & Clinical Trials, Finance & Insurance, Government & Public Statistics, Academia & Research. Very strong—statisticians are in high demand across virtually every industry. The American Statistical Association reports consistent employment growth. Biostat…

Which high-school courses prepare you for Statistics?

Recommended IB courses: HL Mathematics: Analysis and Approaches, HL Physics or HL Economics, HL Computer Science; Recommended AP courses: AP Statistics, AP Calculus BC, AP Computer Science A; Recommended A-Levels: Mathematics, Further Mathematics, Economics or Computer Science.

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