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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 IUC 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 completely free. Study from anywhere in Egypt, the Arab region, and worldwide with full digital flexibility.
| Code | Course | Credits · hours |
|---|---|---|
| PHDS701 | Advanced Statistical Learning Advanced Statistical Learning (PHDS701) is a 6-credit course on PhD Data Science, with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Advanced Statistical Learning as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| PHDS702 | Causal Inference & Experimental Design Causal Inference & Experimental Design (PHDS702) is a 6-credit course on PhD Data Science, with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Causal Inference & Experimental Design as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| PHDS703 | Scalable Data Systems & Computational Methods Scalable Data Systems & Computational Methods (PHDS703) is a 6-credit course on PhD Data Science, with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Scalable Data Systems & Computational Methods as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| PHDS704 | Doctoral Research Ethics & Scientific Writing Doctoral Research Ethics & Scientific Writing (PHDS704) is a 6-credit course on PhD Data Science, with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Doctoral Research Ethics & Scientific Writing as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 6 · 60 h |
| Code | Course | Credits · hours |
|---|---|---|
| PHDS801 | PhD Candidacy Examination PhD Candidacy Examination (PHDS801) is a 0-credit course on PhD Data Science, with 10 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using PhD Candidacy Examination as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 0 |
| PHDS802 | Doctoral Dissertation Research Doctoral Dissertation Research (PHDS802) is a 36-credit course on PhD Data Science, with 360 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Doctoral Dissertation Research as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 36 · 360 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 IUC. 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. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.
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. Imperial University in Cairo 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@iucairo.co.uk.
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