Statistical Science

The University of Oxford offers a rigorous and comprehensive program in Statistical Science designed to equip students with a deep understanding of statistical principles, methodologies, and applications across diverse fields. This programme provides a solid foundation in both theoretical and applied statistics, fostering critical thinking and data analytical skills essential for tackling complex real-world problems. Students will engage with core topics such as probability theory, statistical inference, experimental design, and data analysis, alongside advanced areas like Bayesian methods, machine learning, and computational statistics. The curriculum emphasizes the importance of programming skills, particularly in languages such as R and Python, to enable effective data manipulation and modeling. Throughout the course, students will have opportunities to apply their knowledge through practical projects, workshops, and collaborations with researchers and industry partners, preparing them for careers in academia, data science, finance, healthcare, and technology sectors. The programme is delivered by leading experts in the field, leveraging Oxford’s rich academic environment and cutting-edge research to provide students with an exceptional learning experience. Additionally, students benefit from access to extensive resources, seminars, and networking events, fostering a vibrant scholarly community. The programme is suitable for individuals with a solid background in mathematics or related disciplines who are eager to develop expertise in statistical science. Graduates will leave with the analytical skills and methodological knowledge to design, analyze, and interpret data effectively, making significant contributions to science, policy, and industry. Emphasizing both theoretical understanding and practical competence, the Oxford Statistical Science programme aims to cultivate versatile professionals capable of applying advanced statistical techniques to solve pressing real-world issues.

The MSc offers a broad high-level training in applied and computational statistics, statistical machine learning, and the fundamental principles of statistical inference. Training is delivered through mathematically demanding lectures and problems classes, hands-on practical sessions in the computer laboratory, report writing and dissertation supervision. You will have around three months to work on your dissertation with guidance from your supervisor.

You will be assessed on your performance in written examinations around May, through your work in the assessed practical problems set during the year, and by the quality and depth of your dissertation.

The Department of Statistics has made some changes to the content and delivery of the course and the revised MSc programme is running for the first time in 2016-17. There is now more emphasis on computational statistics and statistical machine learning, more opportunity for students to take courses from the MMath Mathematics and Statistics degree, and enhanced class support. The assessment structure remains the same as in previous years. From 2017-18 the course is known as the MSc in Statistical Science (previously the MSc in Applied Statistics) to better reflect its content.

Students take four, or exceptionally five, courses each term. Three courses each term are core courses and students must complete the practical sessions in these courses.

The options available will vary from year to year. The core courses available each year may also vary. In 2016-17 the core courses are:

• Applied Statistics
• Statistical Inference
• Statistical Programming
• Computational Statistics
• Data Mining and Machine Learning
• Bayes Methods.

In 2016-17 the options are:

• Stochastic Models in Mathematical Genetics
• Probability and Statistics for Network Analysis
• Graphical Models
• Statistical Machine Learning
• Advanced Simulation Methods
• Actuarial Science.

Applicants are normally expected to be predicted or have achieved a first-class or strong upper second-class undergraduate degree with honours (or equivalent international qualifications), as a minimum, in a degree course with substantial advanced mathematical and statistical content; some very quantitative courses in science, social science (notably economics) or medicine may be appropriate if they meet this criterion.

However, entrance to the course is very competitive and most successful applicants have a first-class degree or the equivalent.

For applicants with a degree from the USA, the minimum GPA sought is 3.6 out of 4.0.

If you hold non-UK qualifications and wish to check how your qualifications match these requirements, you can contact the National Recognition Information Centre for the United Kingdom (UK NARIC).

No Graduate Record Examination (GRE) or GMAT scores are sought.

• Official transcript(s)
• CV/résumé
• Personal statement:Up to two pages
• Written work:Either one essay of 4,000 words or two essays of 2,000 words each
• References/letters of recommendation:Three overall, generally academic

ENGLISH LANGUAGE REQUIREMENTS

Higher level

The University of Oxford offers a range of financial support options for students enrolled in the Statistical Science program, designed to help offset tuition fees and living expenses. Students are encouraged to explore scholarships, bursaries, and loan opportunities available through the university and external funding bodies. For home students, Oxford provides the Oxford and Clarendon Scholarships, which are highly competitive and award full or partial funding based on academic merit and financial need. International students can also apply for various scholarships, including the Clarendon Fund, which covers tuition and provides a living stipend for exceptional candidates. Additionally, competitive external fellowships and grants are available through government programs, private foundations, and research organizations aimed at supporting international students and postgraduate researchers. The university also offers access to student loans and financial guidance through the University Careers Service, which helps students navigate bursary applications and budgeting. Students are advised to apply early, as many funding opportunities have strict deadlines and limited awards. In some cases, students might consider part-time work opportunities within the university or local community, although these are often limited by visa regulations for international students. The university's financial support packages are designed to ensure that talented students from diverse backgrounds can pursue their studies without being hindered by financial constraints. For specific information about eligibility criteria, application procedures, and deadlines, students should consult the official University of Oxford financial aid and scholarship webpage dedicated to postgraduate studies. Overall, the university is committed to providing comprehensive support aimed at enabling academic success and the enrichment of its diverse student body through its financial assistance programs.

The Bachelor's degree in Statistical Science at the University of Oxford is a rigorous academic program designed to provide students with a comprehensive understanding of statistical methods, data analysis, and the theoretical foundations of statistics. This programme aims to equip students with both theoretical knowledge and practical skills, enabling them to apply statistical techniques across various disciplines such as economics, medicine, science, and social sciences. The course structure typically includes modules in probability theory, statistical inference, computational statistics, machine learning, and statistical modelling, ensuring graduates are well-prepared for careers in data science, research, or further academic study.

Students engage in a range of learning formats including lectures, tutorials, and practicals that involve working with real datasets. The course emphasizes critical thinking, problem-solving, and the development of analytic skills. The university leverages its extensive research facilities and collaborates with industry and research institutions, providing students with opportunities for internships and collaborative projects. The programme also focuses on the ethical considerations in data handling and analysis, preparing students to confront contemporary challenges in data privacy and responsible usage.

Admission to the programme is highly competitive, requiring strong academic records, particularly in mathematics and sciences, and a demonstrated interest in quantitative methods. Students benefit from the university’s vibrant academic community, access to leading statisticians and data scientists, and opportunities for interdisciplinary collaboration. The degree is typically completed over three to four years, with the possibility of incorporating a year abroad or a research project as part of the curriculum. Graduates of the programme often pursue careers in academia, industry, government, and non-profit organizations, contributing to data-driven decision-making processes across many sectors.