Statistical Science

Statistical Science at the University of Oxford is a comprehensive and rigorous programme designed to equip students with a deep understanding of statistical theories, methodologies, and their applications across various disciplines. This programme integrates theoretical foundations with practical skills, enabling graduates to analyze complex data, develop statistical models, and contribute to decision-making processes in academia, industry, and government. Students will engage with a broad curriculum covering probability, statistical inference, computational methods, and data analysis techniques, supported by instruction from leading researchers in the field. The programme emphasizes both conceptual understanding and hands-on experience, often involving collaborative projects, statistical software, and real-world data sets. As part of Oxford’s world-class humanities and sciences ecosystem, students have access to cutting-edge resources, a vibrant academic community, and opportunities for interdisciplinary research. Graduates of this programme are well-prepared for careers in data science, finance, healthcare, public policy, and further academic research. The programme's rigorous approach ensures that students develop critical thinking, problem-solving abilities, and quantitative skills that are highly sought after in today’s data-driven world. Admission to the programme is competitive, requiring a strong academic background in mathematics or related fields, and applicants are encouraged to demonstrate enthusiasm for analytical thinking and statistical methods. The duration of the course typically spans one to three years, depending on the mode of study, with options for part-time or full-time engagement to accommodate different student needs. Overall, Oxford's Statistical Science programme offers an exceptional educational experience, combining theoretical depth with practical relevance, preparing students to excel in diverse careers or to pursue advanced research in the mathematical sciences.

The PGDip aims to train you to solve real-world statistical problems. When completing the course you should be able to choose an appropriate statistical method to solve a given problem of data analysis, implement the analysis on a computer, and communicate your results clearly and succinctly. 

The course 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 and report writing.

You will be assessed on your performance in two written examinations around May, and through your submitted reports in assessed practical problems set during the year. 

The Department of Statistics has made some changes to the content and delivery of the course and the revised 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 in 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 PGDip in Statistical Science (previously the PGDip 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 highly 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
• References/letters of recommendation:Three overall, generally academic

ENGLISH LANGUAGE REQUIREMENTS

Higher level

The University of Oxford offers a comprehensive range of financing options for students enrolled in its degree programs, including those in Statistical Science. Funding opportunities are designed to support both domestic and international students throughout their studies. The university provides scholarships, bursaries, and grants that are available based on academic merit, financial need, or specific criteria related to the applicant’s background. Some of the most prestigious scholarships include the Rhodes Scholarships, which are highly competitive and offer full funding for outstanding students from various countries. Additionally, there are college-based scholarships that can significantly reduce the financial burden of studying at Oxford, often covering tuition fees and living expenses.

Students are encouraged to explore external funding sources such as government loans, private scholarships, and sponsorships, which can supplement university-provided financial aid. International students may also benefit from government-sponsored schemes or bilateral agreements between their home countries and the UK. The university’s admissions team provides guidance on available funding options and the application processes for each opportunity, ensuring that prospective students understand their eligibility and deadlines.

Financial planning is an integral part of the student experience at Oxford, and the university offers tailored advice through its dedicated financial aid offices and career services. These offices assist students in budgeting for their studies, understanding fee structures, and looking for part-time work opportunities that comply with student visa regulations. The cost of studying at Oxford varies depending on the program, with degree-specific tuition fees set annually. For the Statistical Science program, tuition fees are aligned with those of similar postgraduate science courses, with additional costs for accommodation, living expenses, and study materials.

Students are encouraged to apply early for funding to maximize their chances of receiving financial support. The university’s funding landscape is highly competitive, and successful applicants often demonstrate academic excellence and a strong motivation for their chosen field. Overall, Oxford remains committed to ensuring that talented students from diverse backgrounds can pursue their academic ambitions without undue financial hardship, fostering a diverse and vibrant academic community.

The Bachelor of Arts (BA) in Statistical Science at the University of Oxford is a distinguished undergraduate program designed to provide students with a comprehensive understanding of the fundamental concepts and applications of statistics and data analysis. This rigorous course combines theoretical foundations with practical skills, preparing graduates for careers in diverse fields such as finance, healthcare, government, research, and data science. The curriculum covers core areas including probability theory, statistical inference, computational statistics, and data modelling, enabling students to develop expertise in analyzing complex data sets and interpreting results accurately.

Throughout the program, students engage in a variety of teaching methods, including lectures, seminars, tutorials, and project-based learning. They also have opportunities to work with real-world data sets, applying statistical techniques to practical problems. The course emphasizes critical thinking, quantitative reasoning, and ethical considerations in data management and analysis. Students are encouraged to develop strong programming skills, often using languages such as R and Python, supporting their ability to implement statistical methods efficiently.

Oxford’s Department of Statistics offers a vibrant academic environment with access to world-class faculty members who are leading researchers in the field. The program benefits from close collaboration with other disciplines such as mathematics, computer science, and economics, enriching the learning experience and broadening intellectual perspectives. Students are supported by extensive resources, including state-of-the-art computing facilities and a variety of extracurricular activities, including seminars, research projects, and career development events.

The duration of the course is typically three years full-time. It is characterized by a strong emphasis on both theoretical rigor and applied skills, enabling graduates to work effectively in multidisciplinary teams and adapt to rapidly evolving technological advancements. Upon successful completion, students receive a BA degree in Statistical Science, recognized globally for its academic excellence and rigorous training. Many graduates pursue further studies or embark on professional careers in industry, academia, or government, where analytical skills and statistical expertise are increasingly valued. The program also encourages interdisciplinary research and offers pathways for students interested in specializing further in areas such as machine learning, data analytics, or biostatistics.