PhD Data Science

Overview

The PhD in Data Science is a research doctorate for candidates who will advance methods or applications of data science under faculty supervision. Year 1 builds advanced statistical learning, causal inference, computational methods and research ethics; candidacy examination follows; subsequent years centre on original dissertation research contributing new knowledge. Research clusters include fairness-aware learning, causal ML for policy, large-scale inference and scientific machine learning. PhD Data Science is taught as one award, not a list of unrelated modules. The published length is 3–5 years, and the credit total is 60. Students move from Advanced Statistical Learning through Advanced Statistical Learning; Causal Inference & Experimental Design; Scalable Data Systems & Computational Methods; Doctoral Research Ethics & Scientific Writing; PhD Candidacy Examination; Doctoral Dissertation Research, and finish on Doctoral Dissertation Research. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Contribute original methods or rigorous applications at publication standard Design experiments and identification strategies for complex data problems Build scalable computational approaches for large datasets Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.

Candidates with a Durham MSc Data Science or MSc Artificial Intelligence distinction may streamline coursework by agreement with supervisors. GPU compute and research seminar series support experimental work.

Who This Programme Is For

• ✦MSc graduates pursuing academic research careers
• ✦Industrial researchers deepening methodological contribution
• ✦Statisticians and computer scientists targeting faculty pathways
• ✦Policy researchers specialising in causal data science

Programme Structure & Delivery

Learning Outcomes

• ✦Contribute original methods or rigorous applications at publication standard
• ✦Design experiments and identification strategies for complex data problems
• ✦Build scalable computational approaches for large datasets
• ✦Uphold research ethics and responsible communication of uncertainty
• ✦Mentor junior researchers and teach foundational methods

Career Pathways

• ✦University Faculty / Research Fellow
• ✦Research Scientist (Industry Lab)
• ✦Principal Data Scientist
• ✦Quantitative Researcher
• ✦Science Policy Advisor (Data & AI)

Curriculum