MSc

MSc Operational Research & Analytics

MSc Operational Research & Analytics — an accredited Master's for people doing this work, not just reading about it.

Online or on campus

24 months36 creditsEnglish (dual pathway: Edinburgh campus or tuition-free online; one academic standard)

Overview

Optimisation, simulation, forecasting and decision modelling — the quantitative toolkit for scheduling, logistics, healthcare and public-service planning. The University of Brigant teaches this award to one academic standard whether you study on campus in Edinburgh or on the tuition-free online pathway. Assessment is identity-checked; the marking scheme does not change with your postcode. This is an academic Master's award; where a field has a licensing or registration route, that remains a matter for your professional body or regulator. You will work through the material as a practitioner-researcher: applied methods, real or anonymised cases, and a supervised capstone or dissertation as the final component. Faculty mark whether you noticed the constraints a slide deck usually hides. MSc Operational Research & 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 Statistics & Experimental Design through Statistics & Experimental Design; Data Management, SQL & Pipelines; Programming for Data Analysis; Predictive Modelling & Machine Learning; Causal Inference & A/B Testing; Forecasting & Time Series, and finish on 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: Turn a messy business question into a defensible analysis Choose, fit and validate the right statistical or ML model Build dashboards and pipelines that other people can trust and maintain 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.

The programme carries 36 credits across four assessed modules. Delivery is tuition-free (£0) on the online pathway. Transcripts use letter grades and a GPA scale consistent with University of Brigant awards. No professional-body logo is used in marketing to imply endorsement.

Who This Programme Is For

Analysts and reporting staff moving into modelling
Managers who must run a data or insight function
Professionals in any sector who want to work with data credibly
Graduates with some quantitative background

How you can study

Recommended

Study online

100% tuition-free online

Online tuition is free. Online examinations are free. There is no exam fee for online study.

✦ Tuition: £0 / $0
✦ Examinations are free. There is no exam fee.
✦ Learn from anywhere · Live online seminars

Apply online →

On-campus study

Study at our campus

Prefer to learn at our Edinburgh campus? Campus tuition and campus examinations are charged. The published campus price is on each programme page.

✦ Tuition: £9,500 / US$12,000/year
✦ Location: 3 Lady Lawson St, Edinburgh EH3 9DS, United Kingdom
✦ Campus examinations are charged
✦ In-person seminars, library & student services

Apply for campus →

What You Will Learn

Turn a messy business question into a defensible analysis
Choose, fit and validate the right statistical or ML model
Build dashboards and pipelines that other people can trust and maintain
Communicate uncertainty honestly to decision-makers

Curriculum

Module 1: Analytical Foundations

Code	Course	Credits · hours
BX72401	Statistics & Experimental Design Statistics & Experimental Design (BX72401) is a 3-credit course on MSc Operational Research & 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 Statistics & 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. BX72401-M1 · The question, the unit of analysis, and data quality — Statistics & Experimental Design BX72401-M2 · Features, baselines, and error — Statistics & Experimental Design BX72401-M3 · Validation, leakage, and what the model must not decide — Statistics & Experimental Design BX72401-M4 · Monitoring, drift, and human review — Statistics & Experimental Design BX72401-M5 · Interpretation for a non-technical decision maker — Statistics & Experimental Design BX72401-M6 · A supervised project with method, result, and limitation — Statistics & Experimental Design	3 · 30 h
BX72402	Data Management, SQL & Pipelines Data Management, SQL & Pipelines (BX72402) is a 3-credit course on MSc Operational Research & 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 & Pipelines 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. BX72402-M1 · The question, the unit of analysis, and data quality — Data Management, SQL & Pipelines BX72402-M2 · Features, baselines, and error — Data Management, SQL & Pipelines BX72402-M3 · Validation, leakage, and what the model must not decide — Data Management, SQL & Pipelines BX72402-M4 · Monitoring, drift, and human review — Data Management, SQL & Pipelines BX72402-M5 · Interpretation for a non-technical decision maker — Data Management, SQL & Pipelines BX72402-M6 · A supervised project with method, result, and limitation — Data Management, SQL & Pipelines	3 · 30 h
BX72403	Programming for Data Analysis Programming for Data Analysis (BX72403) is a 3-credit course on MSc Operational Research & 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 Programming for Data Analysis 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. BX72403-M1 · The question, the unit of analysis, and data quality — Programming for Data Analysis BX72403-M2 · Features, baselines, and error — Programming for Data Analysis BX72403-M3 · Validation, leakage, and what the model must not decide — Programming for Data Analysis BX72403-M4 · Monitoring, drift, and human review — Programming for Data Analysis BX72403-M5 · Interpretation for a non-technical decision maker — Programming for Data Analysis BX72403-M6 · A supervised project with method, result, and limitation — Programming for Data Analysis	3 · 30 h

Code

Course

Credits · hours

BX72401

Statistics & Experimental Design

Statistics & Experimental Design (BX72401) is a 3-credit course on MSc Operational Research & 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 Statistics & 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.

BX72401-M1 · The question, the unit of analysis, and data quality — Statistics & Experimental Design
BX72401-M2 · Features, baselines, and error — Statistics & Experimental Design
BX72401-M3 · Validation, leakage, and what the model must not decide — Statistics & Experimental Design
BX72401-M4 · Monitoring, drift, and human review — Statistics & Experimental Design
BX72401-M5 · Interpretation for a non-technical decision maker — Statistics & Experimental Design
BX72401-M6 · A supervised project with method, result, and limitation — Statistics & Experimental Design

3 · 30 h

BX72402

Data Management, SQL & Pipelines

Data Management, SQL & Pipelines (BX72402) is a 3-credit course on MSc Operational Research & 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 & Pipelines 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.

BX72402-M1 · The question, the unit of analysis, and data quality — Data Management, SQL & Pipelines
BX72402-M2 · Features, baselines, and error — Data Management, SQL & Pipelines
BX72402-M3 · Validation, leakage, and what the model must not decide — Data Management, SQL & Pipelines
BX72402-M4 · Monitoring, drift, and human review — Data Management, SQL & Pipelines
BX72402-M5 · Interpretation for a non-technical decision maker — Data Management, SQL & Pipelines
BX72402-M6 · A supervised project with method, result, and limitation — Data Management, SQL & Pipelines

3 · 30 h

BX72403

Programming for Data Analysis

Programming for Data Analysis (BX72403) is a 3-credit course on MSc Operational Research & 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 Programming for Data Analysis 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.

BX72403-M1 · The question, the unit of analysis, and data quality — Programming for Data Analysis
BX72403-M2 · Features, baselines, and error — Programming for Data Analysis
BX72403-M3 · Validation, leakage, and what the model must not decide — Programming for Data Analysis
BX72403-M4 · Monitoring, drift, and human review — Programming for Data Analysis
BX72403-M5 · Interpretation for a non-technical decision maker — Programming for Data Analysis
BX72403-M6 · A supervised project with method, result, and limitation — Programming for Data Analysis

3 · 30 h

Module 2: Modelling & Method

Code	Course	Credits · hours
BX72405	Predictive Modelling & Machine Learning Predictive Modelling & Machine Learning (BX72405) is a 3-credit course on MSc Operational Research & 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 Modelling & Machine 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. BX72405-M1 · The question, the unit of analysis, and data quality — Predictive Modelling & Machine Learning BX72405-M2 · Features, baselines, and error — Predictive Modelling & Machine Learning BX72405-M3 · Validation, leakage, and what the model must not decide — Predictive Modelling & Machine Learning BX72405-M4 · Monitoring, drift, and human review — Predictive Modelling & Machine Learning BX72405-M5 · Interpretation for a non-technical decision maker — Predictive Modelling & Machine Learning BX72405-M6 · A supervised project with method, result, and limitation — Predictive Modelling & Machine Learning	3 · 30 h
BX72406	Causal Inference & A/B Testing Causal Inference & A/B Testing (BX72406) is a 3-credit course on MSc Operational Research & 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 Causal Inference & A/B Testing 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. BX72406-M1 · The question, the unit of analysis, and data quality — Causal Inference & A/B Testing BX72406-M2 · Features, baselines, and error — Causal Inference & A/B Testing BX72406-M3 · Validation, leakage, and what the model must not decide — Causal Inference & A/B Testing BX72406-M4 · Monitoring, drift, and human review — Causal Inference & A/B Testing BX72406-M5 · Interpretation for a non-technical decision maker — Causal Inference & A/B Testing BX72406-M6 · A supervised project with method, result, and limitation — Causal Inference & A/B Testing	3 · 30 h
BX72407	Forecasting & Time Series Forecasting & Time Series (BX72407) is a 3-credit course on MSc Operational Research & 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 Forecasting & Time Series 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. BX72407-M1 · The question, the unit of analysis, and data quality — Forecasting & Time Series BX72407-M2 · Features, baselines, and error — Forecasting & Time Series BX72407-M3 · Validation, leakage, and what the model must not decide — Forecasting & Time Series BX72407-M4 · Monitoring, drift, and human review — Forecasting & Time Series BX72407-M5 · Interpretation for a non-technical decision maker — Forecasting & Time Series BX72407-M6 · A supervised project with method, result, and limitation — Forecasting & Time Series	3 · 30 h

Code

Course

Credits · hours

BX72405

Predictive Modelling & Machine Learning

Predictive Modelling & Machine Learning (BX72405) is a 3-credit course on MSc Operational Research & 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 Modelling & Machine 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.

BX72405-M1 · The question, the unit of analysis, and data quality — Predictive Modelling & Machine Learning
BX72405-M2 · Features, baselines, and error — Predictive Modelling & Machine Learning
BX72405-M3 · Validation, leakage, and what the model must not decide — Predictive Modelling & Machine Learning
BX72405-M4 · Monitoring, drift, and human review — Predictive Modelling & Machine Learning
BX72405-M5 · Interpretation for a non-technical decision maker — Predictive Modelling & Machine Learning
BX72405-M6 · A supervised project with method, result, and limitation — Predictive Modelling & Machine Learning

3 · 30 h

BX72406

Causal Inference & A/B Testing

Causal Inference & A/B Testing (BX72406) is a 3-credit course on MSc Operational Research & 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 Causal Inference & A/B Testing 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.

BX72406-M1 · The question, the unit of analysis, and data quality — Causal Inference & A/B Testing
BX72406-M2 · Features, baselines, and error — Causal Inference & A/B Testing
BX72406-M3 · Validation, leakage, and what the model must not decide — Causal Inference & A/B Testing
BX72406-M4 · Monitoring, drift, and human review — Causal Inference & A/B Testing
BX72406-M5 · Interpretation for a non-technical decision maker — Causal Inference & A/B Testing
BX72406-M6 · A supervised project with method, result, and limitation — Causal Inference & A/B Testing

3 · 30 h

BX72407

Forecasting & Time Series

Forecasting & Time Series (BX72407) is a 3-credit course on MSc Operational Research & 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 Forecasting & Time Series 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.

BX72407-M1 · The question, the unit of analysis, and data quality — Forecasting & Time Series
BX72407-M2 · Features, baselines, and error — Forecasting & Time Series
BX72407-M3 · Validation, leakage, and what the model must not decide — Forecasting & Time Series
BX72407-M4 · Monitoring, drift, and human review — Forecasting & Time Series
BX72407-M5 · Interpretation for a non-technical decision maker — Forecasting & Time Series
BX72407-M6 · A supervised project with method, result, and limitation — Forecasting & Time Series

3 · 30 h

Module 3: Scale & Application

Code	Course	Credits · hours
BX72409	Big Data & Cloud Analytics Big Data & Cloud Analytics (BX72409) is a 3-credit course on MSc Operational Research & 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 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. BX72409-M1 · The question, the unit of analysis, and data quality — Big Data & Cloud Analytics BX72409-M2 · Features, baselines, and error — Big Data & Cloud Analytics BX72409-M3 · Validation, leakage, and what the model must not decide — Big Data & Cloud Analytics BX72409-M4 · Monitoring, drift, and human review — Big Data & Cloud Analytics BX72409-M5 · Interpretation for a non-technical decision maker — Big Data & Cloud Analytics BX72409-M6 · A supervised project with method, result, and limitation — Big Data & Cloud Analytics	3 · 30 h
BX72410	Domain Analytics (Marketing, Finance, Operations) Domain Analytics (Marketing, Finance, Operations) (BX72410) is a 3-credit course on MSc Operational Research & 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 Domain Analytics (Marketing, Finance, Operations) 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. BX72410-M1 · The question, the unit of analysis, and data quality — Domain Analytics (Marketing, Finance, Operations) BX72410-M2 · Features, baselines, and error — Domain Analytics (Marketing, Finance, Operations) BX72410-M3 · Validation, leakage, and what the model must not decide — Domain Analytics (Marketing, Finance, Operations) BX72410-M4 · Monitoring, drift, and human review — Domain Analytics (Marketing, Finance, Operations) BX72410-M5 · Interpretation for a non-technical decision maker — Domain Analytics (Marketing, Finance, Operations) BX72410-M6 · A supervised project with method, result, and limitation — Domain Analytics (Marketing, Finance, Operations)	3 · 30 h
BX72411	Data Governance, Quality & Ethics Data Governance, Quality & Ethics (BX72411) is a 3-credit course on MSc Operational Research & 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 Governance, Quality & Ethics 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. BX72411-M1 · The question, the unit of analysis, and data quality — Data Governance, Quality & Ethics BX72411-M2 · Features, baselines, and error — Data Governance, Quality & Ethics BX72411-M3 · Validation, leakage, and what the model must not decide — Data Governance, Quality & Ethics BX72411-M4 · Monitoring, drift, and human review — Data Governance, Quality & Ethics BX72411-M5 · Interpretation for a non-technical decision maker — Data Governance, Quality & Ethics BX72411-M6 · A supervised project with method, result, and limitation — Data Governance, Quality & Ethics	3 · 30 h

Code

Course

Credits · hours

BX72409

Big Data & Cloud Analytics

Big Data & Cloud Analytics (BX72409) is a 3-credit course on MSc Operational Research & 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 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.

BX72409-M1 · The question, the unit of analysis, and data quality — Big Data & Cloud Analytics
BX72409-M2 · Features, baselines, and error — Big Data & Cloud Analytics
BX72409-M3 · Validation, leakage, and what the model must not decide — Big Data & Cloud Analytics
BX72409-M4 · Monitoring, drift, and human review — Big Data & Cloud Analytics
BX72409-M5 · Interpretation for a non-technical decision maker — Big Data & Cloud Analytics
BX72409-M6 · A supervised project with method, result, and limitation — Big Data & Cloud Analytics

3 · 30 h

BX72410

Domain Analytics (Marketing, Finance, Operations)

Domain Analytics (Marketing, Finance, Operations) (BX72410) is a 3-credit course on MSc Operational Research & 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 Domain Analytics (Marketing, Finance, Operations) 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.

BX72410-M1 · The question, the unit of analysis, and data quality — Domain Analytics (Marketing, Finance, Operations)
BX72410-M2 · Features, baselines, and error — Domain Analytics (Marketing, Finance, Operations)
BX72410-M3 · Validation, leakage, and what the model must not decide — Domain Analytics (Marketing, Finance, Operations)
BX72410-M4 · Monitoring, drift, and human review — Domain Analytics (Marketing, Finance, Operations)
BX72410-M5 · Interpretation for a non-technical decision maker — Domain Analytics (Marketing, Finance, Operations)
BX72410-M6 · A supervised project with method, result, and limitation — Domain Analytics (Marketing, Finance, Operations)

3 · 30 h

BX72411

Data Governance, Quality & Ethics

Data Governance, Quality & Ethics (BX72411) is a 3-credit course on MSc Operational Research & 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 Governance, Quality & Ethics 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.

BX72411-M1 · The question, the unit of analysis, and data quality — Data Governance, Quality & Ethics
BX72411-M2 · Features, baselines, and error — Data Governance, Quality & Ethics
BX72411-M3 · Validation, leakage, and what the model must not decide — Data Governance, Quality & Ethics
BX72411-M4 · Monitoring, drift, and human review — Data Governance, Quality & Ethics
BX72411-M5 · Interpretation for a non-technical decision maker — Data Governance, Quality & Ethics
BX72411-M6 · A supervised project with method, result, and limitation — Data Governance, Quality & Ethics

3 · 30 h

Module 4: Integration & Capstone

Code	Course	Credits · hours
BX72413	Research Design for Analytics Research Design for Analytics (BX72413) is a 3-credit course on MSc Operational Research & 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 Research Design for 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. BX72413-M1 · The question, the unit of analysis, and data quality — Research Design for Analytics BX72413-M2 · Features, baselines, and error — Research Design for Analytics BX72413-M3 · Validation, leakage, and what the model must not decide — Research Design for Analytics BX72413-M4 · Monitoring, drift, and human review — Research Design for Analytics BX72413-M5 · Interpretation for a non-technical decision maker — Research Design for Analytics BX72413-M6 · A supervised project with method, result, and limitation — Research Design for Analytics	3 · 30 h
BX72414	Communicating Analysis to Executives Communicating Analysis to Executives (BX72414) is a 3-credit course on MSc Operational Research & 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 Communicating Analysis to Executives 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. BX72414-M1 · The question, the unit of analysis, and data quality — Communicating Analysis to Executives BX72414-M2 · Features, baselines, and error — Communicating Analysis to Executives BX72414-M3 · Validation, leakage, and what the model must not decide — Communicating Analysis to Executives BX72414-M4 · Monitoring, drift, and human review — Communicating Analysis to Executives BX72414-M5 · Interpretation for a non-technical decision maker — Communicating Analysis to Executives BX72414-M6 · A supervised project with method, result, and limitation — Communicating Analysis to Executives	3 · 30 h
BX72415	Analytics Capstone Project Analytics Capstone Project (BX72415) is a 3-credit course on MSc Operational Research & 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 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. BX72415-M1 · The question, the unit of analysis, and data quality — Analytics Capstone Project BX72415-M2 · Features, baselines, and error — Analytics Capstone Project BX72415-M3 · Validation, leakage, and what the model must not decide — Analytics Capstone Project BX72415-M4 · Monitoring, drift, and human review — Analytics Capstone Project BX72415-M5 · Interpretation for a non-technical decision maker — Analytics Capstone Project BX72415-M6 · A supervised project with method, result, and limitation — Analytics Capstone Project	3 · 30 h

Code

Course

Credits · hours

BX72413

Research Design for Analytics

Research Design for Analytics (BX72413) is a 3-credit course on MSc Operational Research & 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 Research Design for 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.

BX72413-M1 · The question, the unit of analysis, and data quality — Research Design for Analytics
BX72413-M2 · Features, baselines, and error — Research Design for Analytics
BX72413-M3 · Validation, leakage, and what the model must not decide — Research Design for Analytics
BX72413-M4 · Monitoring, drift, and human review — Research Design for Analytics
BX72413-M5 · Interpretation for a non-technical decision maker — Research Design for Analytics
BX72413-M6 · A supervised project with method, result, and limitation — Research Design for Analytics

3 · 30 h

BX72414

Communicating Analysis to Executives

Communicating Analysis to Executives (BX72414) is a 3-credit course on MSc Operational Research & 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 Communicating Analysis to Executives 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.

BX72414-M1 · The question, the unit of analysis, and data quality — Communicating Analysis to Executives
BX72414-M2 · Features, baselines, and error — Communicating Analysis to Executives
BX72414-M3 · Validation, leakage, and what the model must not decide — Communicating Analysis to Executives
BX72414-M4 · Monitoring, drift, and human review — Communicating Analysis to Executives
BX72414-M5 · Interpretation for a non-technical decision maker — Communicating Analysis to Executives
BX72414-M6 · A supervised project with method, result, and limitation — Communicating Analysis to Executives

3 · 30 h

BX72415

Analytics Capstone Project

Analytics Capstone Project (BX72415) is a 3-credit course on MSc Operational Research & 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 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.

BX72415-M1 · The question, the unit of analysis, and data quality — Analytics Capstone Project
BX72415-M2 · Features, baselines, and error — Analytics Capstone Project
BX72415-M3 · Validation, leakage, and what the model must not decide — Analytics Capstone Project
BX72415-M4 · Monitoring, drift, and human review — Analytics Capstone Project
BX72415-M5 · Interpretation for a non-technical decision maker — Analytics Capstone Project
BX72415-M6 · A supervised project with method, result, and limitation — Analytics Capstone Project

3 · 30 h

Career Paths

Data analyst / senior data analyst
Data scientist
Analytics engineer
Business intelligence lead
Analytics or insight manager

Programme Highlights

SQL, statistics, modelling and visualisation end to end
Experimentation and causal inference, not just correlation
A capstone on a real or anonymised dataset
Portfolio-ready deliverables

Assessment

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.

Admission requirements

Academic entry

Bachelor's degree from a recognised institution (minimum 2:2 / GPA 2.5/4.0 or an equivalent national scale)
Identity document for remote identity-checked assessment enrolment

English language

English proficiency: IELTS 5.5, TOEFL 46 or equivalent (waived for prior English-medium study). State your primary study mode without changing the academic standard.

Documents

Statement of Purpose (500 words minimum) covering goals, professional context and fit
Two academic or professional references with verifiable contact details
CV or résumé covering education and relevant work
Official transcripts (digital copies accepted at application; certified copies before award)

Programme questions

How long is the MSc Operational Research & Analytics?

The published duration is 24 months. Teaching language: English (dual pathway: Edinburgh campus or tuition-free online; one academic standard). Actual time-to-complete depends on mode and any recognised prior learning.

Can I study this from my country?

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.

What does it cost?

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.

What are the entry requirements?

Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.

How is the programme assessed?

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.

Will this award be recognised where I live?

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.

How do I apply?

Start an application on this website. Progress is saved from the first step. Admissions: admissions@brigant.uk.

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