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1 Year On Campus Masters Program
Imperial College LondonImperial College London’s Master of Science in Statistics (Data Science) is a rigorous, STEM-designated graduate program offered by the Department of Mathematics. The program provides advanced training in modern statistical methodology, data science techniques, and machine learning, combining strong theoretical foundations with practical applications across diverse industries. Designed for mathematically adept students, it prepares graduates for impactful careers in academia, research, finance, technology, healthcare, and policy.
Located in the heart of London and consistently ranked among the world’s top universities, Imperial leverages its global reputation and industry connections to offer students unparalleled opportunities for learning, networking, and professional development.
Program Format & Duration
Full-time, on-campus (London, UK)
Duration: 1 year (12 months)
Department: Mathematics
Start Date: October
Award: MSc Statistics (Data Science)
STEM-designated
Core Curriculum Components
The curriculum blends rigorous statistical theory with computational tools and real-world data science applications.
Core Modules (Required):
Statistical Foundations
Statistical Inference
Applied Bayesian Methods
Generalised Linear Models
Multivariate Analysis
Computational and Data Science Tools
Programming in R and Python
Computational Statistics
Machine Learning
High-dimensional Statistical Modelling
Electives and Specialist Options (Sample):
Deep Learning
Time Series Analysis
Network Data Analysis
Statistical Finance
Bioinformatics and Genomics
Causal Inference
Spatial Statistics
Research Project (Dissertation)
An independent research project conducted during the summer term under faculty supervision
Focused on a challenging statistical or data science problem
May be theoretical, applied, or completed in collaboration with industry or external research partners
Access to Imperial’s strong network of research groups in statistics, data science, machine learning, and applied mathematics
Hands-on experience through the summer dissertation, often linked to real-world datasets or collaborative projects with industry partners
Opportunities to work with external organizations in finance, healthcare, government, and technology for applied data science challenges
Participation in interdisciplinary research initiatives such as the Data Science Institute and Imperial College Business Analytics Lab
Exposure to cutting-edge tools, programming environments, and high-performance computing resources for large-scale data analysis
Engagement in departmental seminars, workshops, and guest lectures featuring leading academics and industry experts
Potential for internships or consultancy-style projects facilitated by Imperial’s industry connections and career services
Involvement in student-led data competitions, hackathons, and problem-solving challenges across the university
Career pathways in data science, quantitative analysis, statistical consulting, and risk modelling across sectors such as finance, healthcare, technology, and government
Progression into specialist roles like Biostatistician, Machine Learning Specialist, or Data Engineer in research-intensive organizations
Opportunities to transition into leadership positions such as Lead Data Scientist, Quantitative Research Lead, or Head of Analytics
Academic progression to PhD programs in statistics, data science, or related mathematical sciences at Imperial or other top global institutions
Roles in international policy-making, think tanks, and NGOs requiring advanced data analytics expertise
Entrepreneurial ventures in AI-powered analytics, statistical software development, or domain-specific data solutions
Global career mobility due to Imperial’s strong employer reputation and alumni network
Potential to influence decision-making in data-driven strategy at senior and executive levels in diverse industries
Academic Degree: Hold a UK upper second-class honours degree (2:1) or equivalent in a quantitative discipline (e.g., statistics, mathematics, computing, engineering, physics) Quantitative & Programming Background: Demonstrate strong knowledge in mathematical methods, linear algebra, probability/statistics, and programming (Python, R, MATLAB, etc.).Transcripts & References: Provide a complete application including an academic transcript and two references via the online application system, with originals required only upon admissionPersonal Statement: Submit a written personal statement explaining your background, motivations for the course, and academic preparednessEnglish Proficiency: International students must meet minimum scores—IELTS ≥ 7.0, TOEFL ≥ 100, PTE ≥ 69, or Duolingo ≥ 125—or complete a suitable English program Application Fee: Pay an £80 fee when submitting your application online Entry Requirements Compliance: Fulfill degree classification, quantitative background, English proficiency, and references before the final deadline (typically early July for September entry) .Potential Interview: Shortlisted applicants—especially those with non-traditional backgrounds—may be invited for a brief interview (in-person or via video) .Statistician
Data Scientist
Quantitative Analyst
Risk Analyst
Biostatistician
Machine Learning Specialist
Statistical Consultant
Data Analyst
Data Engineer
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