D'Math University | Statistics & Data Science  ·  Programme #11

BSc Statistics

A rigorous three-year undergraduate programme that builds deep fluency in probability theory, statistical inference, and data analysis. Graduates are equipped to turn complex datasets into actionable insights across industry, government, and research.

Undergraduate 3 Years Online & Blended Industry-Linked
30
Core & Elective Modules
£42k
Average Graduate Salary
48+
Partner Universities
95%
Graduate Employment Rate

Programme Overview

The BSc Statistics at D'Math University is structured around three integrated years of study. Year 1 establishes mathematical foundations — probability spaces, descriptive statistics, and introductory inference. Year 2 advances into regression modelling, experimental design, and statistical computing. Year 3 covers multivariate analysis, Bayesian methods, and a substantial applied project with real-world data partnerships.

The programme is accredited by the Royal Statistical Society and aligned with international CBSE and Cambridge advanced frameworks, ensuring global recognisability of your qualification.

What You'll Learn

  • Probability Theory: Axiomatic foundations, distributions, and limit theorems
  • Statistical Inference: Hypothesis testing, confidence intervals, power analysis
  • Regression & Modelling: Linear, generalised, and nonlinear models
  • Bayesian Statistics: Prior/posterior reasoning, MCMC sampling
  • Statistical Computing: R and Python for data analysis and simulation
  • Survey & Experimental Design: Sampling strategies, factorial experiments
  • Multivariate Methods: PCA, cluster analysis, discriminant analysis
  • Applied Statistics: Case studies from healthcare, finance, and social science
Core Curriculum
📊

Probability Theory

Measure-theoretic probability, random variables, expectation, characteristic functions, and central limit theorem proofs.

📈

Statistical Inference

Frequentist estimation (MLE, method of moments), hypothesis testing frameworks, Neyman-Pearson theory.

🔢

Linear Regression

Simple and multiple regression, OLS estimation, diagnostics, variable selection, and regularisation techniques.

🧮

Bayesian Statistics

Bayesian paradigm, conjugate priors, posterior computation, Markov Chain Monte Carlo, and Bayesian model comparison.

📉

Stochastic Processes

Markov chains, Poisson processes, Brownian motion, martingales, and applications in queuing and finance.

🗃️

Data Collection & Survey Design

Questionnaire design, stratified and cluster sampling, non-response bias, census methodology and ethics.

🧪

Experimental Design

Completely randomised, block, and factorial designs; ANOVA; response surface methodology; Latin squares.

💻

Statistical Computing

R and Python programming for data wrangling, simulation, bootstrap methods, and reproducible statistical workflows.

Course Catalogue

Click any course to view its objective and learning outcomes.

STA 101 Probability & Distributions +

Objective

To master probability theory as the foundation for statistical reasoning.

Learning Outcomes

  • Apply probability axioms and conditional probability.
  • Identify common discrete and continuous distributions.
  • Compute moments and MGFs.
  • Use joint, marginal and conditional distributions.
  • Apply transformations of random variables.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
STA 102 Statistical Inference +

Objective

To estimate parameters and test hypotheses on samples.

Learning Outcomes

  • Apply MLE and method of moments.
  • Construct confidence intervals.
  • Conduct hypothesis tests with appropriate reporting.
  • Apply Cramér-Rao to assess estimators.
  • Distinguish frequentist and Bayesian perspectives.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
Interactive Activity — Hypothesis Testing Visualizer
Set null hypothesis μ₀, sample mean x̄, sample size n and σ. The activity computes the z-statistic, p-value (shaded tail), and tells you whether to reject H₀ at significance α.
μ₀ = x̄ = σ = n =
α = 0.050 Tail:
STA 103 Linear Regression +

Objective

To fit, interpret and diagnose linear regression models.

Learning Outcomes

  • Apply OLS to single and multiple regression.
  • Diagnose violations and use remedial measures.
  • Apply variable selection and regularisation.
  • Use prediction intervals.
  • Communicate regression output.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
Interactive Activity — Linear Regression (Drag-and-Drop)
Click the canvas to add scatter points. The least-squares line, equation, and R² update live. Vertical lines from each point show residuals.
Click to add points (need at least 2 for a line).
STA 104 Bayesian Statistics +

Objective

To apply Bayesian reasoning to inference and prediction.

Learning Outcomes

  • Apply Bayes' rule with conjugate priors.
  • Compute posterior distributions.
  • Apply MCMC sampling.
  • Conduct Bayesian model comparison.
  • Discuss the role of prior beliefs.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
Interactive Activity — Bayesian Coin Update
Beta(α, β) prior on a coin's bias. Click "Toss" to flip a coin (true bias hidden) and watch the posterior update via Bayes' rule.
prior α = 1.0 prior β = 1.0
true p =
Toss the coin to start updating the posterior.
STA 201 Stochastic Processes +

Objective

To analyse random processes including Markov chains and Brownian motion.

Learning Outcomes

  • Analyse Markov chains in discrete and continuous time.
  • Apply Poisson processes.
  • Apply Brownian motion in modelling.
  • Use martingale arguments.
  • Apply queueing theory in service systems.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
STA 202 Survey Design & Sampling +

Objective

To design surveys that yield unbiased and precise estimates.

Learning Outcomes

  • Apply simple, stratified and cluster sampling.
  • Compute design weights and standard errors.
  • Address non-response bias.
  • Design questionnaires for valid measurement.
  • Apply ethical principles to data collection.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
STA 203 Experimental Design +

Objective

To design experiments that estimate effects efficiently.

Learning Outcomes

  • Apply CRD, RBD and factorial designs.
  • Use ANOVA to analyse experimental data.
  • Apply response surface methodology.
  • Use Latin and Graeco-Latin squares.
  • Adjust for blocking and confounding.
STA 204 Statistical Computing in R/Python +

Objective

To use software for reproducible statistical analysis.

Learning Outcomes

  • Manipulate data with tidyverse and pandas.
  • Implement bootstrap and Monte Carlo methods.
  • Build R Markdown and Jupyter reports.
  • Implement MCMC samplers.
  • Apply version control to analysis projects.
STA 301 Time Series Analysis +

Objective

To analyse and forecast data observed over time.

Learning Outcomes

  • Decompose series into trend, season and residual.
  • Fit ARIMA and GARCH models.
  • Apply spectral analysis.
  • Use state-space models.
  • Forecast with uncertainty quantification.
Interactive Activity — Sequence Convergence
Pick a sequence and an ε. The graph shows when a_n enters the ε-band around limit L. The smallest such N is the "epsilon-N" for convergence.
a_n = ε = 0.10
Interactive Activity — Epsilon-Delta for Continuity
For f(x) = x², set the point a and tolerance ε. The activity finds the largest δ such that |x − a| < δ ⟹ |f(x) − f(a)| < ε.
a = 1.0 ε = 0.50
STA 302 Multivariate Methods +

Objective

To analyse high-dimensional datasets using multivariate methods.

Learning Outcomes

  • Apply PCA and factor analysis.
  • Use canonical correlation analysis.
  • Apply MANOVA and discriminant analysis.
  • Apply hierarchical and k-means clustering.
  • Visualise multivariate data.
STA 303 Categorical Data Analysis +

Objective

To model count and categorical outcomes.

Learning Outcomes

  • Apply chi-squared and Fisher's exact tests.
  • Fit logistic regression for binary outcomes.
  • Apply multinomial and ordinal regression.
  • Use log-linear models for contingency tables.
  • Apply GEEs for clustered data.
Interactive Activity — Correlation Explorer
Generate 100 sample points with a chosen correlation ρ. Compare Pearson r, Spearman ρ_s, and Kendall τ. See how the scatter pattern changes.
ρ = 0.70
STA 304 Statistics Project +

Objective

To complete a substantial statistics project under supervision.

Learning Outcomes

  • Frame a real-world question statistically.
  • Source and clean appropriate data.
  • Apply rigorous statistical methods.
  • Write a research-style statistical report.
  • Present findings to a non-technical audience.
Career Pathways
📊

Statistician

Design studies, analyse data, and communicate findings for government agencies, research institutions, or private consultancies.

💡

Data Analyst

Extract and interpret structured datasets to support business decisions, KPI tracking, and strategic reporting.

📋

Market Research Analyst

Apply survey methodology and inferential statistics to understand consumer behaviour and market trends.

🏥

Biostatistician

Support clinical trials, epidemiological studies, and pharmaceutical research with robust statistical methodology.

🏛️

Government Analyst

Work within national statistics offices, treasury, or policy departments to inform public decisions with data.

📐

Actuarial Trainee

Begin a path toward professional actuarial qualification, using statistics to model risk in insurance and pensions.

Top Universities Offering This Programme
Harvard University Stanford University Columbia University University College London University of Edinburgh University of Toronto Australian National University National University of Singapore University of Melbourne Lund University

Why D'Math University

01

Theory Meets Practice

Every module pairs rigorous mathematical theory with real-world datasets sourced from our 48+ industry and university partners.

02

RSS-Accredited Curriculum

Our programme meets the full graduate statistician pathway of the Royal Statistical Society, opening doors to chartership.

03

Expert Faculty

Learn from practising statisticians, published researchers, and data professionals with experience at ONS, NHS, and global firms.

04

Global Placement Network

95% of graduates secure employment within six months, supported by our dedicated careers portal and alumni mentorship programme.

Enrol in BSc Statistics →

Applications open year-round — flexible start dates available