MSc

MSc Data Analytics for Business

Data analytics masters UK. 6 assessed courses, 1,800 notional hours, tuition-free.

Online or on campus

18 months180 creditsEnglish

Overview

Data analytics masters UK. MSc Data Analytics for Business at University of Brigant: 6 assessed courses, 180 credits and 1,800 notional learning hours, at 10 hours per credit. The hours on each course add up to that total. Seminars run online. An Edinburgh campus route is published separately from this online award. MSc Data Analytics for Business is taught as one award, not a list of unrelated modules. The published length is 18 months, and the credit total is 180. Students move from Questions, measures, and dirty data through Questions, measures, and dirty data; Analysis and the claim it supports; Dashboards for a decision; Data responsibility; Briefing a non-specialist; Capstone: analytics file, and finish on Capstone: analytics file. 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: Produce a professional file in Data Analytics for Business Choose a method and write what the evidence cannot support Keep a record a third party can review 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.

Each credit is 10 notional learning hours. The courses add up to 180 credits and 1,800 hours.

Who This Programme Is For

People already working around Data Analytics for Business
Career-changers who can study without leaving their city
Teams who need a file they can review, not a slogan

Faculty spotlight

University of Brigant supervises the courses and the final assessed file. Supervision is assigned from the school that owns the award.

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

Produce a professional file in Data Analytics for Business
Choose a method and write what the evidence cannot support
Keep a record a third party can review
State the limit of the award
Submit the final project inside the published hours

Curriculum

Stage 1 — 900 hours

Code	Course	Credits · hours
S9TW901	Questions, measures, and dirty data Questions, measures, and dirty data (S9TW901) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Questions, measures, and dirty data 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. S9TW901-M1 · The question, the unit of analysis, and data quality — Questions, measures, and dirty data S9TW901-M2 · Features, baselines, and error — Questions, measures, and dirty data S9TW901-M3 · Validation, leakage, and what the model must not decide — Questions, measures, and dirty data S9TW901-M4 · Monitoring, drift, and human review — Questions, measures, and dirty data S9TW901-M5 · Interpretation for a non-technical decision maker — Questions, measures, and dirty data S9TW901-M6 · A supervised project with method, result, and limitation — Questions, measures, and dirty data	30 · 300 h
SA3VU02	Analysis and the claim it supports Analysis and the claim it supports (SA3VU02) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Analysis and the claim it supports 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. SA3VU02-M1 · The question, the unit of analysis, and data quality — Analysis and the claim it supports SA3VU02-M2 · Features, baselines, and error — Analysis and the claim it supports SA3VU02-M3 · Validation, leakage, and what the model must not decide — Analysis and the claim it supports SA3VU02-M4 · Monitoring, drift, and human review — Analysis and the claim it supports SA3VU02-M5 · Interpretation for a non-technical decision maker — Analysis and the claim it supports SA3VU02-M6 · A supervised project with method, result, and limitation — Analysis and the claim it supports	30 · 300 h
SADVG03	Dashboards for a decision Dashboards for a decision (SADVG03) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Dashboards for a decision 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. SADVG03-M1 · The question, the unit of analysis, and data quality — Dashboards for a decision SADVG03-M2 · Features, baselines, and error — Dashboards for a decision SADVG03-M3 · Validation, leakage, and what the model must not decide — Dashboards for a decision SADVG03-M4 · Monitoring, drift, and human review — Dashboards for a decision SADVG03-M5 · Interpretation for a non-technical decision maker — Dashboards for a decision SADVG03-M6 · A supervised project with method, result, and limitation — Dashboards for a decision	30 · 300 h

Code

Course

Credits · hours

S9TW901

Questions, measures, and dirty data

Questions, measures, and dirty data (S9TW901) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Questions, measures, and dirty data 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.

S9TW901-M1 · The question, the unit of analysis, and data quality — Questions, measures, and dirty data
S9TW901-M2 · Features, baselines, and error — Questions, measures, and dirty data
S9TW901-M3 · Validation, leakage, and what the model must not decide — Questions, measures, and dirty data
S9TW901-M4 · Monitoring, drift, and human review — Questions, measures, and dirty data
S9TW901-M5 · Interpretation for a non-technical decision maker — Questions, measures, and dirty data
S9TW901-M6 · A supervised project with method, result, and limitation — Questions, measures, and dirty data

30 · 300 h

SA3VU02

Analysis and the claim it supports

Analysis and the claim it supports (SA3VU02) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Analysis and the claim it supports 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.

SA3VU02-M1 · The question, the unit of analysis, and data quality — Analysis and the claim it supports
SA3VU02-M2 · Features, baselines, and error — Analysis and the claim it supports
SA3VU02-M3 · Validation, leakage, and what the model must not decide — Analysis and the claim it supports
SA3VU02-M4 · Monitoring, drift, and human review — Analysis and the claim it supports
SA3VU02-M5 · Interpretation for a non-technical decision maker — Analysis and the claim it supports
SA3VU02-M6 · A supervised project with method, result, and limitation — Analysis and the claim it supports

30 · 300 h

SADVG03

Dashboards for a decision

Dashboards for a decision (SADVG03) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Dashboards for a decision 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.

SADVG03-M1 · The question, the unit of analysis, and data quality — Dashboards for a decision
SADVG03-M2 · Features, baselines, and error — Dashboards for a decision
SADVG03-M3 · Validation, leakage, and what the model must not decide — Dashboards for a decision
SADVG03-M4 · Monitoring, drift, and human review — Dashboards for a decision
SADVG03-M5 · Interpretation for a non-technical decision maker — Dashboards for a decision
SADVG03-M6 · A supervised project with method, result, and limitation — Dashboards for a decision

30 · 300 h

Stage 2 — 900 hours

Code	Course	Credits · hours
S8FY804	Data responsibility Data responsibility (S8FY804) is a 30-credit course on MSc Data Analytics for Business, with 300 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 responsibility 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. S8FY804-M1 · The question, the unit of analysis, and data quality — Data responsibility S8FY804-M2 · Features, baselines, and error — Data responsibility S8FY804-M3 · Validation, leakage, and what the model must not decide — Data responsibility S8FY804-M4 · Monitoring, drift, and human review — Data responsibility S8FY804-M5 · Interpretation for a non-technical decision maker — Data responsibility S8FY804-M6 · A supervised project with method, result, and limitation — Data responsibility	30 · 300 h
S8PXU05	Briefing a non-specialist Briefing a non-specialist (S8PXU05) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Briefing a non-specialist 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. S8PXU05-M1 · The question, the unit of analysis, and data quality — Briefing a non-specialist S8PXU05-M2 · Features, baselines, and error — Briefing a non-specialist S8PXU05-M3 · Validation, leakage, and what the model must not decide — Briefing a non-specialist S8PXU05-M4 · Monitoring, drift, and human review — Briefing a non-specialist S8PXU05-M5 · Interpretation for a non-technical decision maker — Briefing a non-specialist S8PXU05-M6 · A supervised project with method, result, and limitation — Briefing a non-specialist	30 · 300 h
S8ZXG06	Capstone: analytics file Capstone: analytics file (S8ZXG06) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Capstone: analytics file 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. S8ZXG06-M1 · The question, the unit of analysis, and data quality — Capstone: analytics file S8ZXG06-M2 · Features, baselines, and error — Capstone: analytics file S8ZXG06-M3 · Validation, leakage, and what the model must not decide — Capstone: analytics file S8ZXG06-M4 · Monitoring, drift, and human review — Capstone: analytics file S8ZXG06-M5 · Interpretation for a non-technical decision maker — Capstone: analytics file S8ZXG06-M6 · A supervised project with method, result, and limitation — Capstone: analytics file	30 · 300 h

Code

Course

Credits · hours

S8FY804

Data responsibility

Data responsibility (S8FY804) is a 30-credit course on MSc Data Analytics for Business, with 300 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 responsibility 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.

S8FY804-M1 · The question, the unit of analysis, and data quality — Data responsibility
S8FY804-M2 · Features, baselines, and error — Data responsibility
S8FY804-M3 · Validation, leakage, and what the model must not decide — Data responsibility
S8FY804-M4 · Monitoring, drift, and human review — Data responsibility
S8FY804-M5 · Interpretation for a non-technical decision maker — Data responsibility
S8FY804-M6 · A supervised project with method, result, and limitation — Data responsibility

30 · 300 h

S8PXU05

Briefing a non-specialist

Briefing a non-specialist (S8PXU05) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Briefing a non-specialist 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.

S8PXU05-M1 · The question, the unit of analysis, and data quality — Briefing a non-specialist
S8PXU05-M2 · Features, baselines, and error — Briefing a non-specialist
S8PXU05-M3 · Validation, leakage, and what the model must not decide — Briefing a non-specialist
S8PXU05-M4 · Monitoring, drift, and human review — Briefing a non-specialist
S8PXU05-M5 · Interpretation for a non-technical decision maker — Briefing a non-specialist
S8PXU05-M6 · A supervised project with method, result, and limitation — Briefing a non-specialist

30 · 300 h

S8ZXG06

Capstone: analytics file

Capstone: analytics file (S8ZXG06) is a 30-credit course on MSc Data Analytics for Business, with 300 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 Capstone: analytics file 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.

S8ZXG06-M1 · The question, the unit of analysis, and data quality — Capstone: analytics file
S8ZXG06-M2 · Features, baselines, and error — Capstone: analytics file
S8ZXG06-M3 · Validation, leakage, and what the model must not decide — Capstone: analytics file
S8ZXG06-M4 · Monitoring, drift, and human review — Capstone: analytics file
S8ZXG06-M5 · Interpretation for a non-technical decision maker — Capstone: analytics file
S8ZXG06-M6 · A supervised project with method, result, and limitation — Capstone: analytics file

30 · 300 h

Career Paths

Data analyst
Business intelligence analyst
Reporting lead
Analytics translator
Insight manager

Programme Highlights

180 credits · 1,800 notional hours · 6 courses
Course hours add up to the programme total
data analytics masters UK
Seminars run online. An Edinburgh campus route is published separately from this online award.

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

A bachelor's degree from a recognised institution, or equivalent professional standing considered by admissions

Documents

A statement of about 500 words on why this award fits your work
Two references, academic or professional
A CV

Programme questions

How long is the MSc Data Analytics for Business?

The published duration is 18 months. Teaching language: English. 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.

Recognised & Accredited

Ready to begin?MSc Data Analytics for Business

Start an application

Loading…