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Original research at the frontier of learning, causality and scale
Online or on campus
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 Haverton MSc Data Science or MSc Artificial Intelligence distinction may streamline coursework by agreement with supervisors. GPU compute and research seminar series support experimental work.
Faculty spotlight
Dr. Priya Sharma supervises causal ML; Dr. Yuki Tanaka leads computational and representation learning research groups.
Study online
Online tuition is free. Online examinations and certificates are 100% free and sponsored.
On-campus study
Prefer to learn at our London campus? Campus tuition is published on each programme page. All examinations are 100% free of charge.
| Code | Course | Credits · hours |
|---|---|---|
| PHDS701 | Advanced Statistical Learning Advanced Statistical Learning (PHDS701) is a course on PhD Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
| PHDS702 | Causal Inference & Experimental Design Causal Inference & Experimental Design (PHDS702) is a course on PhD Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
| PHDS703 | Scalable Data Systems & Computational Methods Scalable Data Systems & Computational Methods (PHDS703) is a course on PhD Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
| PHDS704 | Doctoral Research Ethics & Scientific Writing Doctoral Research Ethics & Scientific Writing (PHDS704) is a course on PhD Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
| PHDS781 | A research question in data or intelligent systems A research question in data or intelligent systems (PHDS781) is a course on PhD Data Science. It carries 180 credits and 1800 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 180 · 1800 h |
| PHDS782 | Design, theory, and what the study can claim Design, theory, and what the study can claim (PHDS782) is a course on PhD Data Science. It carries 180 credits and 1800 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 180 · 1800 h |
| Code | Course | Credits · hours |
|---|---|---|
| PHDS801 | PhD Candidacy Examination PhD Candidacy Examination (PHDS801) is a course on PhD Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
| PHDS802 | Doctoral Dissertation Research Doctoral Dissertation Research (PHDS802) is a course on PhD Data Science. It carries 30 credits and 300 notional learning hours, at 10 hours per credit. The course is taught at doctoral level. A pass requires a question, a method, evidence a committee can audit, and an explicit limit on the claim. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.
| 30 · 300 h |
Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.
The published duration is 3–5 years. Teaching language: English. Actual time-to-complete depends on mode and any recognised prior learning.
You may study this award fully online from your country, or — where published — on campus at Haverton. Online study does not require a student visa. Campus study may.
Degree tuition for this award is published as £0 / tuition-free on the online pathway. All online degree examinations and certificates are 100% free and sponsored — the only cost for online study is the application fee. Campus tuition and campus examinations are charged separately. Check the Fees page for details.
Requirements are grouped on this page (academic, English, documents, research). Equivalent qualifications are considered. English may be waived after prior English-medium study.
Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. Haverton University publishes verification pages for certificates. We do not claim automatic equivalence in every country.
Start an application on this website. Progress is saved from the first step. Admissions: admissions@haverton.org.
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