D'Math University | Mathematical Sciences
BSc Mathematical Sciences
A broad, interdisciplinary undergraduate programme that spans pure mathematics, applied mathematics, statistics and computing. Offering five pathway options, it gives you the flexibility to tailor your studies while building a strong quantitative foundation valued across every sector.
Undergraduate
3 Years
Online
Broad Curriculum
32
Total Modules
£38k
Average Graduate Salary
42+
Partner Universities
5
Pathway Options
Programme Overview
Programme Overview
- Five pathways: Pure, Applied, Statistics, Data Science, and Computing
- Shared Year 1 core covering algebra, calculus, statistics and programming
- Year 2 and 3 modules chosen from chosen pathway with elective flexibility
- Final year includes an interdisciplinary project or data analysis capstone
- Encourages cross-disciplinary thinking and collaborative problem-solving
- Suitable for students unsure which branch of mathematics to specialise in
- Strong graduate employability across a wide range of sectors
What You'll Learn
- Build rigorous foundations in algebra, calculus and probability
- Develop computational skills in Python, R and statistical software
- Understand mathematical structures underlying data and algorithms
- Apply statistical methods to analyse real-world datasets
- Model and simulate scientific and social phenomena
- Communicate mathematical findings clearly in written and oral form
- Collaborate on quantitative projects in interdisciplinary teams
Core Curriculum
Core Algebra & Calculus
Foundations of algebra, single-variable calculus, sequences, series and introductory proof techniques.
Statistical Methods
Descriptive statistics, probability distributions, regression, hypothesis testing and ANOVA.
Computational Mathematics
Algorithms for numerical computation, programming in Python/R and scientific visualisation.
Mathematical Reasoning
Logic, sets, proof strategies, induction and the language and culture of modern mathematics.
Linear Algebra
Matrices, determinants, eigenvalues, vector spaces and applications in data science and physics.
Probability Theory
Probability axioms, conditional probability, distributions, expectation and the central limit theorem.
Mathematical Modelling
Building and analysing mathematical models for physical, biological and economic systems.
Discrete Mathematics
Graph theory, combinatorics, recurrence relations and Boolean algebra with computing applications.
Course Catalogue
Click any course to view its objective and learning outcomes.
MSC 101 Mathematical Foundations +
Objective
To provide a rigorous foundation in proof, sets and structures for the mathematical sciences.
Learning Outcomes
- Construct direct, contrapositive and inductive proofs.
- Manipulate sets, functions and relations.
- Apply propositional and predicate logic.
- Analyse the cardinality of mathematical structures.
- Communicate proofs in clear written form.
MSC 102 Calculus & Linear Algebra +
Objective
To develop fluency in the differential calculus and matrix algebra used across the sciences.
Learning Outcomes
- Compute derivatives, integrals and Taylor series.
- Solve linear systems via row reduction and matrix inversion.
- Compute and interpret eigenvalues and eigenvectors.
- Apply optimisation techniques in single and several variables.
- Use matrix algebra in scientific applications.
Interactive Activity — Derivative as Slope of Tangent
Drag the slider to move point P along the curve. The tangent line updates — its slope is the derivative.
f(x):
x =
1.00
Interactive Activity — Riemann Sum Approximation
Drag the slider to add more rectangles. Watch the approximation converge to the true integral.
Rectangles n =
8
MSC 103 Probability & Statistics +
Objective
To establish the statistical and probabilistic reasoning required for empirical science.
Learning Outcomes
- Apply probability axioms and conditional probability.
- Identify and use common discrete and continuous distributions.
- Estimate parameters by maximum likelihood.
- Conduct hypothesis tests and report effect sizes.
- Communicate statistical conclusions to scientists.
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
MSC 104 Numerical Methods +
Objective
To compute approximate solutions to mathematical problems using stable algorithms.
Learning Outcomes
- Implement root-finding and interpolation methods.
- Apply numerical integration and differentiation.
- Solve ODEs numerically using Runge-Kutta schemes.
- Estimate computational error and conditioning.
- Implement methods in Python or MATLAB.
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
MSC 201 Discrete Mathematics +
Objective
To develop combinatorial and graph-theoretic reasoning supporting CS and modelling.
Learning Outcomes
- Apply counting principles and inclusion-exclusion.
- Analyse graphs and trees including spanning subgraphs.
- Solve recurrence relations.
- Apply elementary number theory.
- Use boolean logic and combinatorial circuits.
Interactive Activity — Cayley Table Generator
Pick a group; the operation table generates instantly.
Group:
MSC 202 Mathematical Modelling +
Objective
To translate scientific phenomena into mathematical models and analyse them.
Learning Outcomes
- Formulate models from real scientific scenarios.
- Apply dimensional analysis and scaling.
- Solve simple ODE and PDE models.
- Validate models against experimental data.
- Communicate models to a non-mathematical audience.
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
MSC 203 Operations Research +
Objective
To apply mathematical optimisation to industrial and logistic problems.
Learning Outcomes
- Formulate decision problems as linear programmes.
- Solve LPs using the simplex method.
- Apply integer programming to combinatorial problems.
- Use queueing theory in service systems.
- Apply network optimisation to transportation problems.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a =
1.0
b =
0.5
c =
-0.3
d =
1.0
Interactive Activity — Simplex Method on 2D LP
A small 2-variable LP is shown with its feasible polygon. Press Step to walk along vertices increasing the objective. Highlights the current vertex.
MSC 204 Optimisation +
Objective
To find optima of constrained and unconstrained problems.
Learning Outcomes
- Apply gradient and Newton-type algorithms.
- Use Lagrange multipliers and KKT conditions.
- Solve quadratic and convex programmes.
- Apply dynamic programming to staged decisions.
- Implement optimisation in scientific code.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a =
1.0
b =
0.5
c =
-0.3
d =
1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
MSC 301 Differential Equations +
Objective
To solve and analyse ODEs and introductory PDEs.
Learning Outcomes
- Solve linear and non-linear ODEs.
- Apply Laplace transforms and series solutions.
- Analyse stability of equilibria.
- Solve heat, wave and Laplace PDEs.
- Use Fourier methods in PDE solutions.
MSC 302 Statistical Computing +
Objective
To use statistical software for simulation, inference and reporting.
Learning Outcomes
- Implement bootstrap and Monte Carlo methods.
- Use R or Python for reproducible analysis.
- Visualise data and model output.
- Apply MCMC for Bayesian inference.
- Build R Markdown or Jupyter reports.
MSC 303 Data Analysis +
Objective
To analyse modern multivariate datasets using contemporary methods.
Learning Outcomes
- Apply PCA and factor analysis.
- Use clustering and classification methods.
- Build regression models with model selection.
- Visualise multivariate data.
- Communicate findings using effective storytelling.
MSC 304 Mathematical Sciences Project +
Objective
To pursue an interdisciplinary research project applying mathematical methods to a chosen problem.
Learning Outcomes
- Identify a project at the interface of maths and science.
- Apply rigorous methods drawn from across the curriculum.
- Manage timelines and supervisor meetings.
- Write a substantial scientific report.
- Present findings to a multidisciplinary audience.
Career Pathways
Data Scientist
Extract insights from large datasets using machine learning, statistics and visualisation in technology and finance.
Systems Analyst
Analyse and improve information systems and business processes in government, NHS and corporate organisations.
Actuary
Model financial risk in insurance, pensions and investment using advanced probability and statistics.
Software Engineer
Build scalable applications and algorithms, with strong problem-solving grounded in discrete mathematics.
Science Teacher
Inspire students in mathematics and science at secondary school level with a strong subject knowledge base.
Research Associate
Support academic or industrial research projects involving quantitative analysis and mathematical modelling.
Top Global Universities
University College London
University of Leeds
University of Warwick
University of Queensland
University of Auckland
University of Cape Town
University of Toronto
University of Delhi
NUS Singapore
Arizona State University
Why D'Math University
STEP 01
Expert Faculty
Tutored by mathematicians and statisticians with diverse backgrounds spanning academia and industry practice.
STEP 02
Research-Integrated Learning
Engage with real datasets and modelling problems drawn from current research projects and industry challenges.
STEP 03
Industry Connections
Career support and employer connections across data science, finance, government, tech and education sectors.
STEP 04
Flexible Online Delivery
Pathway flexibility and asynchronous content allow you to study at your own pace from anywhere in the world.
Start your interdisciplinary mathematics journey — apply now.