MSc Computational Mathematics and Data Analysis

1 Year On Campus Masters Program

Heriot Watt University Edinburgh

Program Overview

The MSc Computational Mathematics and Data Analysis at Heriot‑Watt gives you the opportunity to become a skilled computational mathematician someone who doesn’t just study abstract theory, but uses mathematics and computing to simulate real-world systems. It’s ideal if you enjoy programming, numerical methods, simulation, modelling, and want a degree that prepares you for roles in industry, research or data‑heavy applications.

Curriculum structure

Year 1 – Foundation & Core Computational Methods
You’ll start with a core module Modelling and Tools, which sets up the computational and mathematical foundations for more advanced work.
Then, depending on your interests, you choose optional modules for example Optimisation, Modelling and Simulation in the Life Sciences, Numerical ODEs, or Statistical Methods in the first semester.
In the second semester you will take Data Assimilation, Numerical Analysis (PDEs), and Artificial Intelligence as mandatory — giving you strong tools in numerical methods, data‑driven computation and computational mathematics.

Research & Dissertation – Applying Skills to Real Problems
In the final phase you’ll complete a masters project and dissertation. This allows you to work on an applied research project — for example simulations of physical or engineering phenomena, mathematical modelling of systems, finite‑element discretization, or computational spectral theory.

Focus areas

“Numerical methods & scientific computing; simulation and computational modelling; data assimilation; PDE & ODE numerical analysis; computational applications in physics, engineering, materials science or life sciences; applied mathematical modelling.”

Learning outcomes

You’ll graduate with the ability to formulate mathematical models of real-world systems, implement and run simulations, apply numerical and computational techniques (for PDEs/ODEs/data assimilation), analyze data with mathematical and statistical tools, and carry out a substantial research or project-based dissertation.

Professional alignment (accreditation / suitability)

This MSc is suitable if you aim for careers in computational science, engineering modelling, data science, quantitative analysis, scientific research or plan to continue to PhD‑level research in applied or computational mathematics. The structured computational and data-analysis focus gives you relevant, in-demand skills for industry, research labs or interdisciplinary work.

Reputation (employability / research strength)

Heriot‑Watt’s mathematics department is well known for its strength in applied and computational mathematics and is part of the broader research environment linked to strong groups in PDEs, dynamical systems, numerical methods and computational science.
Students benefit from a research-active environment, and the programme’s emphasis on real-world simulation and computation increases employability in industry and applied research.

Experiential Learning (Research, Projects, Internships etc.)

Choosing this MSc at Heriot‑Watt means you’ll get more than theory: you’ll build hands‑on computational skills, run serious simulations, and explore modelling that links directly to physics, engineering, data science or industry. You’ll learn to turn mathematical ideas into working code, simulation results, data models preparing you to tackle modern, computationally intensive, real‑world problems.

Here’s how the experience is designed to let you learn by doing:

Computational and simulation‑based coursework — the programme includes mandatory and optional modules such as Modelling and Tools, Numerical Analysis (PDEs), Data Assimilation, Statistical Methods, Optimisation, Numerical ODEs, Modelling & Simulation in Life Sciences, etc.
Realistic research and project work — you’ll complete a Masters project and dissertation in May (end of the year), giving you an opportunity to apply computational mathematics to a topic of interest e.g. finite‑element discretisation of PDEs, spectral theory, domain decomposition, modelling micro‑electromechanical systems, granular flows, or other simulation‑heavy problems.
Strong research‑oriented environment with expert supervision — the programme is embedded in an active applied & computational mathematics research group, whose interests include numerical analysis, PDEs, dynamical systems, stochastic methods, computational biology, data science and more. This gives you a chance to engage with current research, perhaps even collaborate or draw on ongoing projects.
Interdisciplinary and applied‑context exposure — because the research group works on problems spanning physics, biology, engineering, finance, and data science, you get the chance to see how mathematical computation and modelling applies across many fields.
Access to innovation hubs and cross‑disciplinary centres students may benefit from proximity to the university’s broader research institutes and facilities (e.g. robotics, data‑science hubs), offering potential exposure to real‑world computational problems and collaboration beyond pure mathematics.

Progression & Future Opportunities

After finishing this master’s, you’ll leave with powerful skills in mathematical modelling, simulation, computation and data analysis making you a strong candidate for technical, research, or data‑driven roles in many sectors (industry, finance, engineering, research, etc.). The degree gives you flexibility: you could go into industry now, or keep studying if you like research and deep problem-solving. So:

What this degree can lead to

Typical roles you could aim for: Computational / Data Scientist, Quantitative / Statistical / Risk / Financial Analyst, Simulation & Modelling Engineer, R&D Analyst (in engineering, physics, materials science, environmental modelling), Machine Learning Engineer.
University support & environment: Heriot‑Watt has a strong Department of Mathematics with decades of experience in applied and computational maths they’re part of a research‑active community doing work on numerical analysis, fluid dynamics, data science, machine learning, and more.
Practical, real‑world modelling & project experience: The MSc involves coursework (modelling, numerical analysis, data assimilation, AI/ML methods, PDEs, etc.) plus a substantial masters project often involving real problems (e.g. simulations of physical systems, micro‑mechanical systems, large‑scale numerical methods).
Applicability across sectors: Because the skills are computational and mathematical but broadly applicable, you could find work in finance, engineering, data science, environmental modelling, robotics & AI, and more giving you a broad canvas to choose from depending on your interests.
Graduate outcomes and institutional reputation: Heriot‑Watt promotes good graduate outcomes more of their graduates are employed or in further education than many other institutions in Scotland. Their applied/computational maths research is also nationally and internationally recognised.

Further Academic Progression:

If you love mathematics or computational modelling and want to go deeper, this MSc provides a strong launchpad for doctoral research (PhD) in applied mathematics, computational science, data science / machine learning, or interdisciplinary research (e.g. physics‑math‑engineering, environmental modelling, AI/robotics). With the research‑active faculty and connections at Heriot‑Watt, you’ll get supervision and a strong foundation to build a long‑term research career.