MSc

MSc Data Science & Product Management (Dual Track)

MSc Data Science & Product Management — one admission, a shared core, two named specialisations.

Online or on campus

24 months42 creditsEnglish (dual pathway: Edinburgh campus or tuition-free online; one academic standard)

MSc Data Science & Product Management (Dual Track)

Overview

A double-path master's: a shared analytical core, then two named pathways — Data Science (modelling, ML engineering, experimentation) and Product Management (discovery, roadmapping, metrics) — closing with a dual-track capstone that ships a data-informed product decision. For analysts moving into product and PMs who need real quantitative depth. 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 Data Science & Product Management (Dual Track) is taught as one award, not a list of unrelated modules. The published length is 24 months, and the credit total is 42. Students move from Statistics & Experimental Design for Decisions through Statistics & Experimental Design for Decisions; Data Engineering & Analytics Foundations; Product Discovery, Users & Value; Research Methods & Identity-Checked Assessment; Machine Learning Theory & Practice; Predictive Modelling & Forecasting, and finish on Dual-Track Capstone: Data-Informed Product Decision. 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 42 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

Term 1 — Shared Analytical & Product Core

Code	Course	Credits · hours
DSPM701	Statistics & Experimental Design for Decisions Statistics & Experimental Design for Decisions (DSPM701) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 for Decisions 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. DSPM701-M1 · The question, the unit of analysis, and data quality — Statistics & Experimental Design for Decisions DSPM701-M2 · Features, baselines, and error — Statistics & Experimental Design for Decisions DSPM701-M3 · Validation, leakage, and what the model must not decide — Statistics & Experimental Design for Decisions DSPM701-M4 · Monitoring, drift, and human review — Statistics & Experimental Design for Decisions DSPM701-M5 · Interpretation for a non-technical decision maker — Statistics & Experimental Design for Decisions DSPM701-M6 · A supervised project with method, result, and limitation — Statistics & Experimental Design for Decisions	3 · 30 h
DSPM702	Data Engineering & Analytics Foundations Data Engineering & Analytics Foundations (DSPM702) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Engineering & Analytics Foundations 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. DSPM702-M1 · The question, the unit of analysis, and data quality — Data Engineering & Analytics Foundations DSPM702-M2 · Features, baselines, and error — Data Engineering & Analytics Foundations DSPM702-M3 · Validation, leakage, and what the model must not decide — Data Engineering & Analytics Foundations DSPM702-M4 · Monitoring, drift, and human review — Data Engineering & Analytics Foundations DSPM702-M5 · Interpretation for a non-technical decision maker — Data Engineering & Analytics Foundations DSPM702-M6 · A supervised project with method, result, and limitation — Data Engineering & Analytics Foundations	3 · 30 h
DSPM703	Product Discovery, Users & Value Product Discovery, Users & Value (DSPM703) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Product Discovery, Users & Value 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. DSPM703-M1 · The question, the unit of analysis, and data quality — Product Discovery, Users & Value DSPM703-M2 · Features, baselines, and error — Product Discovery, Users & Value DSPM703-M3 · Validation, leakage, and what the model must not decide — Product Discovery, Users & Value DSPM703-M4 · Monitoring, drift, and human review — Product Discovery, Users & Value DSPM703-M5 · Interpretation for a non-technical decision maker — Product Discovery, Users & Value DSPM703-M6 · A supervised project with method, result, and limitation — Product Discovery, Users & Value	3 · 30 h
DSPM704	Research Methods & Identity-Checked Assessment Research Methods & Identity-Checked Assessment (DSPM704) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Methods & Identity-Checked Assessment 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. DSPM704-M1 · The question, the unit of analysis, and data quality — Research Methods & Identity-Checked Assessment DSPM704-M2 · Features, baselines, and error — Research Methods & Identity-Checked Assessment DSPM704-M3 · Validation, leakage, and what the model must not decide — Research Methods & Identity-Checked Assessment DSPM704-M4 · Monitoring, drift, and human review — Research Methods & Identity-Checked Assessment DSPM704-M5 · Interpretation for a non-technical decision maker — Research Methods & Identity-Checked Assessment DSPM704-M6 · A supervised project with method, result, and limitation — Research Methods & Identity-Checked Assessment	3 · 30 h

Term 2 — Pathway A: Data Science

Code	Course	Credits · hours
DSPM705	Machine Learning Theory & Practice Machine Learning Theory & Practice (DSPM705) is a 3-credit course on MSc Data Science & Product Management (Dual Track), with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Machine Learning Theory & Practice 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. DSPM705-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice DSPM705-M2 · Features, baselines, and error — Machine Learning Theory & Practice DSPM705-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice DSPM705-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice DSPM705-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice DSPM705-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	3 · 30 h
DSPM706	Predictive Modelling & Forecasting Predictive Modelling & Forecasting (DSPM706) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 & Forecasting as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. DSPM706-M1 · The question, the unit of analysis, and data quality — Predictive Modelling & Forecasting DSPM706-M2 · Features, baselines, and error — Predictive Modelling & Forecasting DSPM706-M3 · Validation, leakage, and what the model must not decide — Predictive Modelling & Forecasting DSPM706-M4 · Monitoring, drift, and human review — Predictive Modelling & Forecasting DSPM706-M5 · Interpretation for a non-technical decision maker — Predictive Modelling & Forecasting DSPM706-M6 · A supervised project with method, result, and limitation — Predictive Modelling & Forecasting	3 · 30 h
DSPM707	ML Systems, Deployment & Monitoring ML Systems, Deployment & Monitoring (DSPM707) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 ML Systems, Deployment & Monitoring 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. DSPM707-M1 · The question, the unit of analysis, and data quality — ML Systems, Deployment & Monitoring DSPM707-M2 · Features, baselines, and error — ML Systems, Deployment & Monitoring DSPM707-M3 · Validation, leakage, and what the model must not decide — ML Systems, Deployment & Monitoring DSPM707-M4 · Monitoring, drift, and human review — ML Systems, Deployment & Monitoring DSPM707-M5 · Interpretation for a non-technical decision maker — ML Systems, Deployment & Monitoring DSPM707-M6 · A supervised project with method, result, and limitation — ML Systems, Deployment & Monitoring	3 · 30 h
DSPM708	Causal Inference & Uplift Modelling Causal Inference & Uplift Modelling (DSPM708) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 & Uplift Modelling 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. DSPM708-M1 · The question, the unit of analysis, and data quality — Causal Inference & Uplift Modelling DSPM708-M2 · Features, baselines, and error — Causal Inference & Uplift Modelling DSPM708-M3 · Validation, leakage, and what the model must not decide — Causal Inference & Uplift Modelling DSPM708-M4 · Monitoring, drift, and human review — Causal Inference & Uplift Modelling DSPM708-M5 · Interpretation for a non-technical decision maker — Causal Inference & Uplift Modelling DSPM708-M6 · A supervised project with method, result, and limitation — Causal Inference & Uplift Modelling	3 · 30 h

Term 3 — Pathway B: Product Management

Code	Course	Credits · hours
DSPM709	Roadmapping, Prioritisation & OKRs Roadmapping, Prioritisation & OKRs (DSPM709) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Roadmapping, Prioritisation & OKRs 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. DSPM709-M1 · The question, the unit of analysis, and data quality — Roadmapping, Prioritisation & OKRs DSPM709-M2 · Features, baselines, and error — Roadmapping, Prioritisation & OKRs DSPM709-M3 · Validation, leakage, and what the model must not decide — Roadmapping, Prioritisation & OKRs DSPM709-M4 · Monitoring, drift, and human review — Roadmapping, Prioritisation & OKRs DSPM709-M5 · Interpretation for a non-technical decision maker — Roadmapping, Prioritisation & OKRs DSPM709-M6 · A supervised project with method, result, and limitation — Roadmapping, Prioritisation & OKRs	3 · 30 h
DSPM710	Metrics, Analytics & Growth Experiments Metrics, Analytics & Growth Experiments (DSPM710) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Metrics, Analytics & Growth Experiments 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. DSPM710-M1 · The question, the unit of analysis, and data quality — Metrics, Analytics & Growth Experiments DSPM710-M2 · Features, baselines, and error — Metrics, Analytics & Growth Experiments DSPM710-M3 · Validation, leakage, and what the model must not decide — Metrics, Analytics & Growth Experiments DSPM710-M4 · Monitoring, drift, and human review — Metrics, Analytics & Growth Experiments DSPM710-M5 · Interpretation for a non-technical decision maker — Metrics, Analytics & Growth Experiments DSPM710-M6 · A supervised project with method, result, and limitation — Metrics, Analytics & Growth Experiments	3 · 30 h
DSPM711	Pricing, Packaging & Go-to-Market Pricing, Packaging & Go-to-Market (DSPM711) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Pricing, Packaging & Go-to-Market 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. DSPM711-M1 · The question, the unit of analysis, and data quality — Pricing, Packaging & Go-to-Market DSPM711-M2 · Features, baselines, and error — Pricing, Packaging & Go-to-Market DSPM711-M3 · Validation, leakage, and what the model must not decide — Pricing, Packaging & Go-to-Market DSPM711-M4 · Monitoring, drift, and human review — Pricing, Packaging & Go-to-Market DSPM711-M5 · Interpretation for a non-technical decision maker — Pricing, Packaging & Go-to-Market DSPM711-M6 · A supervised project with method, result, and limitation — Pricing, Packaging & Go-to-Market	3 · 30 h

Code

Course

Credits · hours

DSPM709

Roadmapping, Prioritisation & OKRs

Roadmapping, Prioritisation & OKRs (DSPM709) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Roadmapping, Prioritisation & OKRs 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.

DSPM709-M1 · The question, the unit of analysis, and data quality — Roadmapping, Prioritisation & OKRs
DSPM709-M2 · Features, baselines, and error — Roadmapping, Prioritisation & OKRs
DSPM709-M3 · Validation, leakage, and what the model must not decide — Roadmapping, Prioritisation & OKRs
DSPM709-M4 · Monitoring, drift, and human review — Roadmapping, Prioritisation & OKRs
DSPM709-M5 · Interpretation for a non-technical decision maker — Roadmapping, Prioritisation & OKRs
DSPM709-M6 · A supervised project with method, result, and limitation — Roadmapping, Prioritisation & OKRs

3 · 30 h

DSPM710

Metrics, Analytics & Growth Experiments

Metrics, Analytics & Growth Experiments (DSPM710) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Metrics, Analytics & Growth Experiments 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.

DSPM710-M1 · The question, the unit of analysis, and data quality — Metrics, Analytics & Growth Experiments
DSPM710-M2 · Features, baselines, and error — Metrics, Analytics & Growth Experiments
DSPM710-M3 · Validation, leakage, and what the model must not decide — Metrics, Analytics & Growth Experiments
DSPM710-M4 · Monitoring, drift, and human review — Metrics, Analytics & Growth Experiments
DSPM710-M5 · Interpretation for a non-technical decision maker — Metrics, Analytics & Growth Experiments
DSPM710-M6 · A supervised project with method, result, and limitation — Metrics, Analytics & Growth Experiments

3 · 30 h

DSPM711

Pricing, Packaging & Go-to-Market

Pricing, Packaging & Go-to-Market (DSPM711) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Pricing, Packaging & Go-to-Market 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.

DSPM711-M1 · The question, the unit of analysis, and data quality — Pricing, Packaging & Go-to-Market
DSPM711-M2 · Features, baselines, and error — Pricing, Packaging & Go-to-Market
DSPM711-M3 · Validation, leakage, and what the model must not decide — Pricing, Packaging & Go-to-Market
DSPM711-M4 · Monitoring, drift, and human review — Pricing, Packaging & Go-to-Market
DSPM711-M5 · Interpretation for a non-technical decision maker — Pricing, Packaging & Go-to-Market
DSPM711-M6 · A supervised project with method, result, and limitation — Pricing, Packaging & Go-to-Market

3 · 30 h

Term 4 — Integration & Dual-Track Capstone

Code	Course	Credits · hours
DSPM712	Integrating Two Disciplines in Practice Integrating Two Disciplines in Practice (DSPM712) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Integrating Two Disciplines in Practice 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. DSPM712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice DSPM712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice DSPM712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice DSPM712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice DSPM712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice DSPM712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice	3 · 30 h
DSPM713	Dual-Track Capstone: Data-Informed Product Decision Dual-Track Capstone: Data-Informed Product Decision (DSPM713) is a 6-credit course on MSc Data Science & Product Management (Dual Track), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Dual-Track Capstone: Data-Informed Product 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. DSPM713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Data-Informed Product Decision DSPM713-M2 · Features, baselines, and error — Dual-Track Capstone: Data-Informed Product Decision DSPM713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Data-Informed Product Decision DSPM713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Data-Informed Product Decision DSPM713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Data-Informed Product Decision DSPM713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Data-Informed Product Decision	6 · 60 h

Code

Course

Credits · hours

DSPM712

Integrating Two Disciplines in Practice

Integrating Two Disciplines in Practice (DSPM712) is a 3-credit course on MSc Data Science & Product Management (Dual Track), 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 Integrating Two Disciplines in Practice 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.

DSPM712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice
DSPM712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice
DSPM712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice
DSPM712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice
DSPM712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice
DSPM712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice

3 · 30 h

DSPM713

Dual-Track Capstone: Data-Informed Product Decision

Dual-Track Capstone: Data-Informed Product Decision (DSPM713) is a 6-credit course on MSc Data Science & Product Management (Dual Track), with 60 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Dual-Track Capstone: Data-Informed Product 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.

DSPM713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Data-Informed Product Decision
DSPM713-M2 · Features, baselines, and error — Dual-Track Capstone: Data-Informed Product Decision
DSPM713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Data-Informed Product Decision
DSPM713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Data-Informed Product Decision
DSPM713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Data-Informed Product Decision
DSPM713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Data-Informed Product Decision

6 · 60 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 Data Science & Product Management (Dual Track)?

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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