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 months180 creditsEnglish (dual pathway: London 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 Haverton University teaches this award to one academic standard whether you study on campus in London 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 Haverton University 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

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Study online

100% tuition-free online

Online tuition is free. Online examinations and certificates are 100% free and sponsored.

✦ Tuition: £0 / $0
✦ Examinations & certificates are 100% free & sponsored
✦ Learn from anywhere · Live online seminars

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On-campus study

Study at our campus

Prefer to learn at our London campus? Campus tuition is published on each programme page. All examinations are 100% free of charge.

✦ Tuition: £9,500 / US$12,000/year
✦ Location: Du Cane Rd, London W12 0NN, United Kingdom
✦ Campus examinations are charged
✦ In-person seminars, library & student services

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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 course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. 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	15 · 150 h
BX72402	Data Management, SQL & Pipelines Data Management, SQL & Pipelines (BX72402) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. 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	15 · 150 h
BX72403	Programming for Data Analysis Programming for Data Analysis (BX72403) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX72403-M1 · The institution, profession, or market the work sits in BX72403-M2 · Who decides, and with what authority BX72403-M3 · A rule, standard, or budget constraint BX72403-M4 · A stakeholder who can stop the work BX72403-M5 · How this setting differs from a generic case BX72403-M6 · What transfers poorly from another country or sector BX72403-M7 · The professional limit of the award BX72403-M8 · The submission: a context briefing with sources. The assessed course is Machine learning, baselines, and leakage.	15 · 150 h

Code

Course

Credits · hours

BX72401

Statistics & Experimental Design

Statistics & Experimental Design (BX72401) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

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

15 · 150 h

BX72402

Data Management, SQL & Pipelines

Data Management, SQL & Pipelines (BX72402) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

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

15 · 150 h

BX72403

Programming for Data Analysis

Programming for Data Analysis (BX72403) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX72403-M1 · The institution, profession, or market the work sits in
BX72403-M2 · Who decides, and with what authority
BX72403-M3 · A rule, standard, or budget constraint
BX72403-M4 · A stakeholder who can stop the work
BX72403-M5 · How this setting differs from a generic case
BX72403-M6 · What transfers poorly from another country or sector
BX72403-M7 · The professional limit of the award
BX72403-M8 · The submission: a context briefing with sources. The assessed course is Machine learning, baselines, and leakage.

15 · 150 h

Module 2: Modelling & Method

Code	Course	Credits · hours
BX72405	Predictive Modelling & Machine Learning Predictive Modelling & Machine Learning (BX72405) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. 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	15 · 150 h
BX72406	Causal Inference & A/B Testing Causal Inference & A/B Testing (BX72406) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX72406-M1 · The duty, rule, or code that binds the work BX72406-M2 · A decision and the person who bears the harm BX72406-M3 · The difference between a legal fact and a justification BX72406-M4 · Evidence, not a values statement BX72406-M5 · What this course does not license the graduate to do BX72406-M6 · A case where the obvious action is the wrong one BX72406-M7 · How the limit is written into the recommendation BX72406-M8 · The submission: an ethics and limits memorandum. The assessed course is Data engineering and reproducible pipelines.	15 · 150 h
BX72407	Forecasting & Time Series Forecasting & Time Series (BX72407) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX72407-M1 · The change being proposed and the person who must carry it BX72407-M2 · The current process and the step that would change BX72407-M3 · Resources, time, and what is not available BX72407-M4 · Resistance and a sequence that respects it BX72407-M5 · A risk if the change is done badly BX72407-M6 · How the designer would know the change happened BX72407-M7 · What the plan must not promise BX72407-M8 · The submission: an implementation note with the risk. The assessed course is Responsible use, governance, and human review.	15 · 150 h

Code

Course

Credits · hours

BX72405

Predictive Modelling & Machine Learning

Predictive Modelling & Machine Learning (BX72405) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

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

15 · 150 h

BX72406

Causal Inference & A/B Testing

Causal Inference & A/B Testing (BX72406) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX72406-M1 · The duty, rule, or code that binds the work
BX72406-M2 · A decision and the person who bears the harm
BX72406-M3 · The difference between a legal fact and a justification
BX72406-M4 · Evidence, not a values statement
BX72406-M5 · What this course does not license the graduate to do
BX72406-M6 · A case where the obvious action is the wrong one
BX72406-M7 · How the limit is written into the recommendation
BX72406-M8 · The submission: an ethics and limits memorandum. The assessed course is Data engineering and reproducible pipelines.

15 · 150 h

BX72407

Forecasting & Time Series

Forecasting & Time Series (BX72407) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX72407-M1 · The change being proposed and the person who must carry it
BX72407-M2 · The current process and the step that would change
BX72407-M3 · Resources, time, and what is not available
BX72407-M4 · Resistance and a sequence that respects it
BX72407-M5 · A risk if the change is done badly
BX72407-M6 · How the designer would know the change happened
BX72407-M7 · What the plan must not promise
BX72407-M8 · The submission: an implementation note with the risk. The assessed course is Responsible use, governance, and human review.

15 · 150 h

Module 3: Scale & Application

Code	Course	Credits · hours
BX72409	Big Data & Cloud Analytics Big Data & Cloud Analytics (BX72409) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX72409-M1 · The standard the work will be judged against BX72409-M2 · The evidence for and against the preferred option BX72409-M3 · A competing explanation BX72409-M4 · A risk that survives the recommendation BX72409-M5 · What a successful-looking result would still not prove BX72409-M6 · Who uses the evaluation and how it could be misused BX72409-M7 · The revision after a critical question BX72409-M8 · The submission: an evaluation with the competing explanation. The assessed course is Deployment, monitoring, and drift.	15 · 150 h
BX72410	Domain Analytics (Marketing, Finance, Operations) Domain Analytics (Marketing, Finance, Operations) (BX72410) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. 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)	15 · 150 h
BX72411	Data Governance, Quality & Ethics Data Governance, Quality & Ethics (BX72411) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. 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	15 · 150 h

Code

Course

Credits · hours

BX72409

Big Data & Cloud Analytics

Big Data & Cloud Analytics (BX72409) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX72409-M1 · The standard the work will be judged against
BX72409-M2 · The evidence for and against the preferred option
BX72409-M3 · A competing explanation
BX72409-M4 · A risk that survives the recommendation
BX72409-M5 · What a successful-looking result would still not prove
BX72409-M6 · Who uses the evaluation and how it could be misused
BX72409-M7 · The revision after a critical question
BX72409-M8 · The submission: an evaluation with the competing explanation. The assessed course is Deployment, monitoring, and drift.

15 · 150 h

BX72410

Domain Analytics (Marketing, Finance, Operations)

Domain Analytics (Marketing, Finance, Operations) (BX72410) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

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)

15 · 150 h

BX72411

Data Governance, Quality & Ethics

Data Governance, Quality & Ethics (BX72411) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

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

15 · 150 h

Module 4: Integration & Capstone

Code	Course	Credits · hours
BX72413	Research Design for Analytics Research Design for Analytics (BX72413) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX72413-M1 · The question and the evidence that could answer it BX72413-M2 · A method that fits that question BX72413-M3 · A weaker method and why it was set aside BX72413-M4 · The source, sample, or case boundary BX72413-M5 · A threat to the validity of the result BX72413-M6 · What the method cannot show BX72413-M7 · How the result should be written for a marker BX72413-M8 · The submission: a methods note with the limit. The assessed course is Statistical inference and a mistaken claim.	15 · 150 h
BX72414	Communicating Analysis to Executives Communicating Analysis to Executives (BX72414) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. 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	15 · 150 h
BX72415	Analytics Capstone Project Analytics Capstone Project (BX72415) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX72415-M1 · The indicator and the unit it counts BX72415-M2 · The source of the data and how it fails BX72415-M3 · Bias, missing data, or a definition that flatters the result BX72415-M4 · A comparison and the assumption inside it BX72415-M5 · A figure a marker can recompute BX72415-M6 · What the number must not be used to decide BX72415-M7 · How uncertainty is reported BX72415-M8 · The submission: a measurement note with the error. The assessed course is Evaluation, error analysis, and harm.	15 · 150 h

Code

Course

Credits · hours

BX72413

Research Design for Analytics

Research Design for Analytics (BX72413) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX72413-M1 · The question and the evidence that could answer it
BX72413-M2 · A method that fits that question
BX72413-M3 · A weaker method and why it was set aside
BX72413-M4 · The source, sample, or case boundary
BX72413-M5 · A threat to the validity of the result
BX72413-M6 · What the method cannot show
BX72413-M7 · How the result should be written for a marker
BX72413-M8 · The submission: a methods note with the limit. The assessed course is Statistical inference and a mistaken claim.

15 · 150 h

BX72414

Communicating Analysis to Executives

Communicating Analysis to Executives (BX72414) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

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

15 · 150 h

BX72415

Analytics Capstone Project

Analytics Capstone Project (BX72415) is a course on MSc Operational Research & Analytics. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX72415-M1 · The indicator and the unit it counts
BX72415-M2 · The source of the data and how it fails
BX72415-M3 · Bias, missing data, or a definition that flatters the result
BX72415-M4 · A comparison and the assumption inside it
BX72415-M5 · A figure a marker can recompute
BX72415-M6 · What the number must not be used to decide
BX72415-M7 · How uncertainty is reported
BX72415-M8 · The submission: a measurement note with the error. The assessed course is Evaluation, error analysis, and harm.

15 · 150 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: London 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 Haverton. 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. 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.

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. Haverton University 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@haverton.org.

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