Computational Statistics and Machine Learning MSc

1 Year On Campus Masters Program

University College London

Program Overview

The MSc Computational Statistics and Machine Learning at University College London combines rigorous training in statistical modelling and machine learning, preparing students for research-intensive roles or industry data science and ML work. It suits those with strong quantitative backgrounds interested in data science, AI, or computational statistics.

Curriculum Structure

In the first part of the year, students take Supervised Learning and Statistical Models and Data Analysis, where they learn classical and modern algorithms, inferential statistics, and how to model data using distributions and regression/GLM approaches.
In the next phase, optional modules such as Graphical Models, Probabilistic and Unsupervised Learning, Machine Vision or Statistical Natural Language Processing allow deep dives into probabilistic modelling, unsupervised algorithms, computer vision or language processing — tailoring the MSc to different ML-oriented career paths.
The programme concludes with the MSc Computational Statistics and Machine Learning Project, an independent dissertation or industry-linked project where students apply their skills to real-world or research-level data, building a portfolio of applied work.

Focus areas
“Statistical modelling, supervised & unsupervised learning, probabilistic models, data analysis, machine learning applications, research project”

Learning outcomes (in a string):
“Master statistical and machine learning techniques; build, evaluate and apply predictive models; conduct rigorous empirical or industry-linked data projects; apply ML/statistics across domains; prepare for PhD or data-science/ML roles.”

Professional alignment (accreditation):
Tailored for research or industry roles in data science, AI, machine learning, or computational statistics — strong foundation for PhD admission or technical roles in tech, finance, health-data, etc.

Reputation (employability ranking):
UCL is ranked among the top 10 globally in the latest QS World University Rankings 2026; its Computer Science department is ranked first in England and second in the UK for research power (REF 2021) — offering strong academic prestige and high employability for graduates.

Experiential Learning (Research, Projects, Internships etc.)

Students gain practical skills through project-based learning, using UCL's high-performance computing facilities and working with real-world datasets from industry and research partners. The programme emphasizes implementing algorithms and statistical models in Python and R, with access to specialized computing resources like the Myriad High Performance Computing facility. This hands-on approach is structured around several key components:

Core Software & Programming: Intensive use of Python, R, and C++ for implementing statistical and machine learning methods.
Computing Facilities: Access to UCL's Myriad High Performance Computing cluster (Linux-based) and the Department of Statistical Science's computing labs.
Group Projects: A significant team-based project where students solve a complex data analysis problem, simulating a real-world industrial or research collaboration.
Research Dissertation: An individual research project often linked to ongoing research within the UCL Centre for Artificial Intelligence or with external industrial partners.
Digital Tools & Libraries: Use of standard ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn) and statistical packages for advanced data analysis.

Progression & Future Opportunities

Graduates of the MSc Computational Statistics and Machine Learning at UCL secure high-demand roles such as machine learning engineer, data scientist, quantitative analyst, and AI researcher at leading tech and finance firms:

UCL's Careers Service delivers CV workshops, mock interviews, employer networking events, and tailored job placement support for data science roles.
Exceptional employability with graduates at Amazon, Apple, McKinsey, Microsoft, and UBS; salaries typically exceed £50,000 starting.
Partnerships with Gatsby Computational Neuroscience Unit and industry leaders provide research projects addressing real-world data challenges.
The rigorous programme builds enduring expertise in machine learning, statistics, and AI, highly valued across tech, finance, and research sectors.
Graduates complete substantial dissertations tackling industrial or cutting-edge research problems, enhancing portfolio strength.

Further Academic Progression: Graduates can pursue PhDs at top institutions like Cambridge or UCL, or specialised research roles in AI and computational statistics.

Additional Information & Requirements

A minimum of an upper second-class UK Bachelor's degree in a highly quantitative subject such as computer science, mathematics, electrical engineering, or physicals sciences.

Relevant work experience may also be considered. Additionally, applicants must be comfortable with undergraduate mathematics in areas such as linear algebra and calculus.

India A Bachelor's degree with a minimum overall CGPA of 7.5/10, 6.0/8.0, 5.5/7.0, 4.0/6.0, 3.0/4.0 or 60% where a GPA scale is not used. The overall mark required is dependent on the ranking of the institution at which the degree has been taken

USA- Bachelor's degree with a final CGPA of 3.3/4.0

IELTS- Overall score of 7.0 and a minimum of 6.5 in each component

TOEFL- Overall score of 96 with 24/30 in reading and writing and 22/30 in speaking and listening

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