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Lead with data. Decide with confidence.
Online or on campus
The MSc Business Analytics at Brigant is designed for ambitious analysts, product leaders, and consultants who need more than dashboards — you must frame decisions, build reproducible pipelines, deploy models responsibly, and communicate findings to executives under uncertainty. Unlike a pure data science degree, this programme emphasises **business impact**: marketing analytics, financial forecasting, prescriptive optimisation, and analytics governance aligned with UK GDPR and EU AI Act literacy. Each module combines faculty-led video sessions, case-based assignments, and tools used in enterprise analytics teams (SQL, Python, Power BI/Tableau, cloud warehouses). The capstone is a sponsored consulting engagement with a live brief, faculty mentorship, and industry panel review. MSc Business Analytics 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 Business Analytics Foundations & Decision Science through Business Analytics Foundations & Decision Science; Statistical Methods for Business Analytics; Data Management, SQL & Analytics Engineering; Data Visualization & Executive Storytelling; Predictive Analytics & Machine Learning for Business; Optimization & Prescriptive Analytics, and finish on MSc Business Analytics Capstone Project. 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: Frame complex business problems as structured analytics engagements with clear KPIs Apply statistical inference, regression, and experimentation to commercial decisions Engineer trustworthy SQL pipelines and dimensional models for BI and ML 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 is built for professionals who must influence decisions — not only build models. You will maintain a portfolio repository (SQL, notebooks, dashboards, governance artefacts) reviewed by faculty and presented to an industry panel in the capstone. Brigant partners with retail and fintech sponsors for anonymised capstone datasets.
Faculty spotlight
Dr. Raj Patel leads cloud analytics architecture; Professor Margaret Ashford supervises capstone engagements with emphasis on executive communication and governance.
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 |
|---|---|---|
| MSBA501 | Business Analytics Foundations & Decision Science Business Analytics Foundations & Decision Science (MSBA501) is a 3-credit course on MSc Business Analytics, 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 Business Analytics Foundations & Decision 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 |
| MSBA502 | Statistical Methods for Business Analytics Statistical Methods for Business Analytics (MSBA502) is a 3-credit course on MSc Business Analytics, 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 Statistical Methods for Business Analytics 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 |
| MSBA503 | Data Management, SQL & Analytics Engineering Data Management, SQL & Analytics Engineering (MSBA503) is a 3-credit course on MSc Business Analytics, 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 Management, SQL & Analytics Engineering 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 |
| MSBA504 | Data Visualization & Executive Storytelling Data Visualization & Executive Storytelling (MSBA504) is a 3-credit course on MSc Business Analytics, 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 Visualization & Executive 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 |
|---|---|---|
| MSBA505 | Predictive Analytics & Machine Learning for Business Predictive Analytics & Machine Learning for Business (MSBA505) is a 3-credit course on MSc Business Analytics, 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 Predictive Analytics & Machine Learning for Business 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 |
| MSBA506 | Optimization & Prescriptive Analytics Optimization & Prescriptive Analytics (MSBA506) is a 3-credit course on MSc Business Analytics, 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 Optimization & Prescriptive Analytics 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 |
| MSBA507 | Big Data, Cloud Analytics & Modern Data Platforms Big Data, Cloud Analytics & Modern Data Platforms (MSBA507) is a 3-credit course on MSc Business Analytics, 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, Cloud Analytics & Modern Data Platforms 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 |
| MSBA508 | Marketing, Customer & Digital Analytics Marketing, Customer & Digital Analytics (MSBA508) is a 3-credit course on MSc Business Analytics, 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 Marketing, Customer & Digital Analytics 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 |
| MSBA509 | Financial Analytics, Forecasting & Risk Modeling Financial Analytics, Forecasting & Risk Modeling (MSBA509) is a 3-credit course on MSc Business Analytics, 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 Financial Analytics, Forecasting & Risk Modeling 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 |
|---|---|---|
| MSBA601 | Analytics Strategy, Ethics, Governance & AI Policy Analytics Strategy, Ethics, Governance & AI Policy (MSBA601) is a 3-credit course on MSc Business Analytics, 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 Analytics Strategy, Ethics, Governance & AI Policy 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 |
| MSBA602 | MSc Business Analytics Capstone Project MSc Business Analytics Capstone Project (MSBA602) is a 6-credit course on MSc Business Analytics, 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 Business Analytics Capstone Project 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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