Data Science and Machine Learning MSc

1 Year On Campus Masters Program

University College London

Program Overview

The MSc Data Science and Machine Learning at University College London offers rigorous training in statistical science, machine learning and data-analysis methods, preparing students for high-demand roles in AI, data science, research or industry. It suits applicants with strong quantitative backgrounds who want to build competence in ML, programming, and applied data science.

Curriculum Structure

In the first term, students take Introduction to Statistical Data Science and Applied Machine Learning, which give them a foundation in statistics, data handling, predictive modelling, and practical machine-learning techniques. In the following term, the curriculum features optional advanced modules — like Graphical Models, Statistical Natural Language Processing, Machine Vision, or Reinforcement Learning — allowing students to specialise in different ML or data-science domains and deepen their theoretical and computational skills. The programme concludes with the MSc Data Science and Machine Learning Project, a supervised dissertation or industry-linked project in which students apply their learning to real-world or research problems.

Focus areas
“Statistical data science, machine learning, predictive modelling, advanced ML methods (NLP, vision, reinforcement), big-data analytics, empirical project work”

Learning outcomes
“Develop strong statistical and machine-learning skills; build and evaluate predictive models; specialise in advanced ML areas; manage, analyse and draw insight from large data; conduct independent data-science projects; prepare for industry or research roles”

Professional alignment (accreditation):
The programme is designed to meet the needs of industry and research employers in data science, machine learning, and AI — providing technical and applied competencies required for high-skilled data roles or doctoral study in related fields.

Reputation (employability rankings):
UCL is globally ranked — among the top universities worldwide — and its Computer Science & Statistical Science departments are widely recognised for research excellence and strong industry links, giving graduates high visibility and competitive advantage for data-science and ML roles.

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 machine learning systems and data science pipelines in Python, with access to specialized computing resources like the Myriad High Performance Computing facility. This applied learning is structured around several key components:

Core Software & Programming: Intensive use of Python and its core data science libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch), with potential use of Spark for large-scale data processing.
Computing Facilities: Access to UCL's Myriad High Performance Computing cluster and the Department of Computer Science's computing labs for demanding computational tasks.
Group Projects: A significant team-based software engineering project focused on building a complete, scalable data science and machine learning system.
Research Dissertation: An individual research project (MSc thesis) often linked to ongoing research within UCL's Centre for Artificial Intelligence or with external industrial partners.
Digital Tools & Platforms: Use of cloud platforms and version control systems like Git for collaborative software development and model deployment.

Progression & Future Opportunities

Graduates of the MSc Data Science and Machine Learning at UCL achieve exceptional employability, securing roles such as data scientist, machine learning engineer, AI specialist, and quantitative analyst at top tech, finance, and research organisations:

UCL's Careers Service offers CV workshops, mock interviews, employer fairs, and dedicated data science recruitment events with firms like Google and Goldman Sachs.
100% in graduate-level work/study within 15 months, with average starting salaries £50,000–£75,000.
Partnerships with Alan Turing Institute and industry leaders provide real-world projects and placements in AI/data challenges.
Rigorous training in advanced ML, statistics, and scalable computing ensures enduring value in fast-evolving tech sectors.
Graduates excel in industry dissertations and competitions, building strong portfolios for competitive markets.

Further Academic Progression: Graduates can pursue PhDs in AI/ML at leading institutions or specialised research fellowships in data science and computational statistics.

Additional Information & Requirements

A minimum of an upper second-class UK Bachelor's degree (or international qualification of an equivalent standard) in a highly quantitative subject such as computer science, mathematics, engineering, physicals, or statistics.

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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