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Turn data into decisions that matter
Online or on campus
Data science sits at the intersection of statistics, computing, and domain expertise. This programme teaches you to frame business questions as analytical problems, wrangle messy real-world datasets, and communicate findings to non-technical audiences. Modules cover Bayesian inference, deep learning, NLP, and MLOps. You will work with public datasets from healthcare, finance, and climate science, building a portfolio that demonstrates end-to-end analytical capability. MSc Data Science is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 36. Students move from Statistics & Probability for Data Science through Statistics & Probability for Data Science; Programming for Analytics (Python/R); Data Wrangling & SQL; Data Visualisation & Storytelling; Machine Learning Fundamentals; Deep Learning & Neural Networks, and finish on MSc Data Science Capstone. 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: Apply statistical inference and experimental design to business problems Build and validate predictive models using scikit-learn, PyTorch, and R Design dashboards and narratives that drive executive action 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.
Every module uses real datasets — never toy CSVs alone. You will document reproducible notebooks and present to a mock executive panel in the capstone. Brigant partners with public-health NGOs for anonymised analytics projects.
Faculty spotlight
Dr. Priya Sharma, ex-lead data scientist at a global bank, teaches MLOps; Professor Daniel Mensah specialises in causal inference for policy evaluation.
Study online
Online tuition is free. Online examinations are free. There is no exam fee for online study.
On-campus study
Prefer to learn at our Edinburgh campus? Campus tuition and campus examinations are charged. The published campus price is on each programme page.
| Code | Course | Credits · hours |
|---|---|---|
| DS501 | Statistics & Probability for Data Science Statistics & Probability for Data Science (DS501) is a 3-credit course on MSc Data Science, with 30 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 Statistics & Probability for Data Science 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.
| 3 · 30 h |
| DS502 | Programming for Analytics (Python/R) Programming for Analytics (Python/R) (DS502) is a 3-credit course on MSc Data Science, with 30 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 Programming for Analytics (Python/R) 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.
| 3 · 30 h |
| DS503 | Data Wrangling & SQL Data Wrangling & SQL (DS503) is a 3-credit course on MSc Data Science, with 30 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 Data Wrangling & SQL 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.
| 3 · 30 h |
| DS504 | Data Visualisation & Storytelling Data Visualisation & Storytelling (DS504) is a 3-credit course on MSc Data Science, with 30 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 Data Visualisation & Storytelling 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.
| 3 · 30 h |
| Code | Course | Credits · hours |
|---|---|---|
| DS505 | Machine Learning Fundamentals Machine Learning Fundamentals (DS505) is a 3-credit course on MSc Data Science, with 30 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 Machine Learning Fundamentals 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.
| 3 · 30 h |
| DS506 | Deep Learning & Neural Networks Deep Learning & Neural Networks (DS506) is a 3-credit course on MSc Data Science, with 30 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 Deep Learning & Neural Networks 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.
| 3 · 30 h |
| DS507 | Natural Language Processing Natural Language Processing (DS507) is a 3-credit course on MSc Data Science, with 30 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 Natural Language Processing 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.
| 3 · 30 h |
| DS508 | Time Series & Forecasting Time Series & Forecasting (DS508) is a 3-credit course on MSc Data Science, with 30 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 Time Series & Forecasting 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.
| 3 · 30 h |
| Code | Course | Credits · hours |
|---|---|---|
| DS601 | MLOps & Production Systems MLOps & Production Systems (DS601) is a 3-credit course on MSc Data Science, with 30 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 MLOps & Production Systems 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.
| 3 · 30 h |
| DS602 | Big Data Technologies (Spark) Big Data Technologies (Spark) (DS602) is a 3-credit course on MSc Data Science, with 30 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 Big Data Technologies (Spark) 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.
| 3 · 30 h |
| DS603 | MSc Data Science Capstone MSc Data Science Capstone (DS603) is a 6-credit course on MSc 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 MSc Data Science Capstone 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 |
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 24 months. 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 Brigant. 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). 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. University of Brigant 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@brigant.uk.
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