MSc

MSc Data Science & Product Analytics (Dual Track)

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

Online only

24 months42 creditsEnglish (ES/PT/HI support hubs do not change the language of assessment unless a bilingual pathway is stated)

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MSc Data Science & Product Analytics (Dual Track)

Overview

A double-path master's: a shared analytical core, then two pathways — Data Science (modelling, ML systems, causal methods) and Product Analytics (funnels, experimentation, retention) — closing with a capstone that turns product data into a shipped decision. Fully online. GlobalMind University delivers this award fully online across six world regions. There is no campus gate. Assessment is identity-checked and one academic standard applies to every student, in every time zone. 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 Analytics (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 through Statistics & Experimental Design; Data Engineering & Analytics Foundations; Product Sense & Metrics Design; Research Methods & Identity-Checked Assessment; Machine Learning Theory & Practice; Predictive Modelling & Forecasting, and finish on Dual-Track Capstone: Product Data to Shipped 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 aside from the examination fee. Transcripts use letter grades and a GPA scale consistent with GlobalMind 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

Digital only

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Tuition is free. Examinations are free. There is no exam fee. Study from anywhere in the world.

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✦ Examinations are free. There is no exam fee.
✦ Learn from anywhere · Live online seminars

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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

Term 1 — Shared Analytical Core

Code	Course	Credits · hours
GMDPA701	Statistics & Experimental Design Statistics & Experimental Design (GMDPA701) is a 3-credit course on MSc Data Science & Product Analytics (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 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. GMDPA701-M1 · The question, the unit of analysis, and data quality — Statistics & Experimental Design GMDPA701-M2 · Features, baselines, and error — Statistics & Experimental Design GMDPA701-M3 · Validation, leakage, and what the model must not decide — Statistics & Experimental Design GMDPA701-M4 · Monitoring, drift, and human review — Statistics & Experimental Design GMDPA701-M5 · Interpretation for a non-technical decision maker — Statistics & Experimental Design GMDPA701-M6 · A supervised project with method, result, and limitation — Statistics & Experimental Design	3 · 30 h
GMDPA702	Data Engineering & Analytics Foundations Data Engineering & Analytics Foundations (GMDPA702) is a 3-credit course on MSc Data Science & Product Analytics (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. GMDPA702-M1 · The question, the unit of analysis, and data quality — Data Engineering & Analytics Foundations GMDPA702-M2 · Features, baselines, and error — Data Engineering & Analytics Foundations GMDPA702-M3 · Validation, leakage, and what the model must not decide — Data Engineering & Analytics Foundations GMDPA702-M4 · Monitoring, drift, and human review — Data Engineering & Analytics Foundations GMDPA702-M5 · Interpretation for a non-technical decision maker — Data Engineering & Analytics Foundations GMDPA702-M6 · A supervised project with method, result, and limitation — Data Engineering & Analytics Foundations	3 · 30 h
GMDPA703	Product Sense & Metrics Design Product Sense & Metrics Design (GMDPA703) is a 3-credit course on MSc Data Science & Product Analytics (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 Sense & Metrics Design as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GMDPA703-M1 · The question, the unit of analysis, and data quality — Product Sense & Metrics Design GMDPA703-M2 · Features, baselines, and error — Product Sense & Metrics Design GMDPA703-M3 · Validation, leakage, and what the model must not decide — Product Sense & Metrics Design GMDPA703-M4 · Monitoring, drift, and human review — Product Sense & Metrics Design GMDPA703-M5 · Interpretation for a non-technical decision maker — Product Sense & Metrics Design GMDPA703-M6 · A supervised project with method, result, and limitation — Product Sense & Metrics Design	3 · 30 h
GMDPA704	Research Methods & Identity-Checked Assessment Research Methods & Identity-Checked Assessment (GMDPA704) is a 3-credit course on MSc Data Science & Product Analytics (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. GMDPA704-M1 · The question, the unit of analysis, and data quality — Research Methods & Identity-Checked Assessment GMDPA704-M2 · Features, baselines, and error — Research Methods & Identity-Checked Assessment GMDPA704-M3 · Validation, leakage, and what the model must not decide — Research Methods & Identity-Checked Assessment GMDPA704-M4 · Monitoring, drift, and human review — Research Methods & Identity-Checked Assessment GMDPA704-M5 · Interpretation for a non-technical decision maker — Research Methods & Identity-Checked Assessment GMDPA704-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
GMDPA705	Machine Learning Theory & Practice Machine Learning Theory & Practice (GMDPA705) is a 3-credit course on MSc Data Science & Product Analytics (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. GMDPA705-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice GMDPA705-M2 · Features, baselines, and error — Machine Learning Theory & Practice GMDPA705-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice GMDPA705-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice GMDPA705-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice GMDPA705-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	3 · 30 h
GMDPA706	Predictive Modelling & Forecasting Predictive Modelling & Forecasting (GMDPA706) is a 3-credit course on MSc Data Science & Product Analytics (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. GMDPA706-M1 · The question, the unit of analysis, and data quality — Predictive Modelling & Forecasting GMDPA706-M2 · Features, baselines, and error — Predictive Modelling & Forecasting GMDPA706-M3 · Validation, leakage, and what the model must not decide — Predictive Modelling & Forecasting GMDPA706-M4 · Monitoring, drift, and human review — Predictive Modelling & Forecasting GMDPA706-M5 · Interpretation for a non-technical decision maker — Predictive Modelling & Forecasting GMDPA706-M6 · A supervised project with method, result, and limitation — Predictive Modelling & Forecasting	3 · 30 h
GMDPA707	ML Systems, Deployment & Monitoring ML Systems, Deployment & Monitoring (GMDPA707) is a 3-credit course on MSc Data Science & Product Analytics (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. GMDPA707-M1 · The question, the unit of analysis, and data quality — ML Systems, Deployment & Monitoring GMDPA707-M2 · Features, baselines, and error — ML Systems, Deployment & Monitoring GMDPA707-M3 · Validation, leakage, and what the model must not decide — ML Systems, Deployment & Monitoring GMDPA707-M4 · Monitoring, drift, and human review — ML Systems, Deployment & Monitoring GMDPA707-M5 · Interpretation for a non-technical decision maker — ML Systems, Deployment & Monitoring GMDPA707-M6 · A supervised project with method, result, and limitation — ML Systems, Deployment & Monitoring	3 · 30 h
GMDPA708	Causal Inference & Experimentation at Scale Causal Inference & Experimentation at Scale (GMDPA708) is a 3-credit course on MSc Data Science & Product Analytics (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 & Experimentation at Scale 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. GMDPA708-M1 · The question, the unit of analysis, and data quality — Causal Inference & Experimentation at Scale GMDPA708-M2 · Features, baselines, and error — Causal Inference & Experimentation at Scale GMDPA708-M3 · Validation, leakage, and what the model must not decide — Causal Inference & Experimentation at Scale GMDPA708-M4 · Monitoring, drift, and human review — Causal Inference & Experimentation at Scale GMDPA708-M5 · Interpretation for a non-technical decision maker — Causal Inference & Experimentation at Scale GMDPA708-M6 · A supervised project with method, result, and limitation — Causal Inference & Experimentation at Scale	3 · 30 h

Term 3 — Pathway B: Product Analytics

Code	Course	Credits · hours
GMDPA709	Funnels, Cohorts & Retention Analysis Funnels, Cohorts & Retention Analysis (GMDPA709) is a 3-credit course on MSc Data Science & Product Analytics (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 Funnels, Cohorts & Retention Analysis as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. GMDPA709-M1 · The question, the unit of analysis, and data quality — Funnels, Cohorts & Retention Analysis GMDPA709-M2 · Features, baselines, and error — Funnels, Cohorts & Retention Analysis GMDPA709-M3 · Validation, leakage, and what the model must not decide — Funnels, Cohorts & Retention Analysis GMDPA709-M4 · Monitoring, drift, and human review — Funnels, Cohorts & Retention Analysis GMDPA709-M5 · Interpretation for a non-technical decision maker — Funnels, Cohorts & Retention Analysis GMDPA709-M6 · A supervised project with method, result, and limitation — Funnels, Cohorts & Retention Analysis	3 · 30 h
GMDPA710	Experiment Design & Decision Frameworks Experiment Design & Decision Frameworks (GMDPA710) is a 3-credit course on MSc Data Science & Product Analytics (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 Experiment Design & Decision Frameworks 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. GMDPA710-M1 · The question, the unit of analysis, and data quality — Experiment Design & Decision Frameworks GMDPA710-M2 · Features, baselines, and error — Experiment Design & Decision Frameworks GMDPA710-M3 · Validation, leakage, and what the model must not decide — Experiment Design & Decision Frameworks GMDPA710-M4 · Monitoring, drift, and human review — Experiment Design & Decision Frameworks GMDPA710-M5 · Interpretation for a non-technical decision maker — Experiment Design & Decision Frameworks GMDPA710-M6 · A supervised project with method, result, and limitation — Experiment Design & Decision Frameworks	3 · 30 h
GMDPA711	Analytics Instrumentation & Governance Analytics Instrumentation & Governance (GMDPA711) is a 3-credit course on MSc Data Science & Product Analytics (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 Analytics Instrumentation & Governance 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. GMDPA711-M1 · The question, the unit of analysis, and data quality — Analytics Instrumentation & Governance GMDPA711-M2 · Features, baselines, and error — Analytics Instrumentation & Governance GMDPA711-M3 · Validation, leakage, and what the model must not decide — Analytics Instrumentation & Governance GMDPA711-M4 · Monitoring, drift, and human review — Analytics Instrumentation & Governance GMDPA711-M5 · Interpretation for a non-technical decision maker — Analytics Instrumentation & Governance GMDPA711-M6 · A supervised project with method, result, and limitation — Analytics Instrumentation & Governance	3 · 30 h

Code

Course

Credits · hours

GMDPA709

Funnels, Cohorts & Retention Analysis

Funnels, Cohorts & Retention Analysis (GMDPA709) is a 3-credit course on MSc Data Science & Product Analytics (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 Funnels, Cohorts & Retention Analysis as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.

GMDPA709-M1 · The question, the unit of analysis, and data quality — Funnels, Cohorts & Retention Analysis
GMDPA709-M2 · Features, baselines, and error — Funnels, Cohorts & Retention Analysis
GMDPA709-M3 · Validation, leakage, and what the model must not decide — Funnels, Cohorts & Retention Analysis
GMDPA709-M4 · Monitoring, drift, and human review — Funnels, Cohorts & Retention Analysis
GMDPA709-M5 · Interpretation for a non-technical decision maker — Funnels, Cohorts & Retention Analysis
GMDPA709-M6 · A supervised project with method, result, and limitation — Funnels, Cohorts & Retention Analysis

3 · 30 h

GMDPA710

Experiment Design & Decision Frameworks

Experiment Design & Decision Frameworks (GMDPA710) is a 3-credit course on MSc Data Science & Product Analytics (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 Experiment Design & Decision Frameworks 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.

GMDPA710-M1 · The question, the unit of analysis, and data quality — Experiment Design & Decision Frameworks
GMDPA710-M2 · Features, baselines, and error — Experiment Design & Decision Frameworks
GMDPA710-M3 · Validation, leakage, and what the model must not decide — Experiment Design & Decision Frameworks
GMDPA710-M4 · Monitoring, drift, and human review — Experiment Design & Decision Frameworks
GMDPA710-M5 · Interpretation for a non-technical decision maker — Experiment Design & Decision Frameworks
GMDPA710-M6 · A supervised project with method, result, and limitation — Experiment Design & Decision Frameworks

3 · 30 h

GMDPA711

Analytics Instrumentation & Governance

Analytics Instrumentation & Governance (GMDPA711) is a 3-credit course on MSc Data Science & Product Analytics (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 Analytics Instrumentation & Governance 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.

GMDPA711-M1 · The question, the unit of analysis, and data quality — Analytics Instrumentation & Governance
GMDPA711-M2 · Features, baselines, and error — Analytics Instrumentation & Governance
GMDPA711-M3 · Validation, leakage, and what the model must not decide — Analytics Instrumentation & Governance
GMDPA711-M4 · Monitoring, drift, and human review — Analytics Instrumentation & Governance
GMDPA711-M5 · Interpretation for a non-technical decision maker — Analytics Instrumentation & Governance
GMDPA711-M6 · A supervised project with method, result, and limitation — Analytics Instrumentation & Governance

3 · 30 h

Term 4 — Integration & Dual-Track Capstone

Code	Course	Credits · hours
GMDPA712	Integrating Two Disciplines in Practice Integrating Two Disciplines in Practice (GMDPA712) is a 3-credit course on MSc Data Science & Product Analytics (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. GMDPA712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice GMDPA712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice GMDPA712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice GMDPA712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice GMDPA712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice GMDPA712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice	3 · 30 h
GMDPA713	Dual-Track Capstone: Product Data to Shipped Decision Dual-Track Capstone: Product Data to Shipped Decision (GMDPA713) is a 6-credit course on MSc Data Science & Product Analytics (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: Product Data to Shipped 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. GMDPA713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Product Data to Shipped Decision GMDPA713-M2 · Features, baselines, and error — Dual-Track Capstone: Product Data to Shipped Decision GMDPA713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Product Data to Shipped Decision GMDPA713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Product Data to Shipped Decision GMDPA713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Product Data to Shipped Decision GMDPA713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Product Data to Shipped Decision	6 · 60 h

Code

Course

Credits · hours

GMDPA712

Integrating Two Disciplines in Practice

Integrating Two Disciplines in Practice (GMDPA712) is a 3-credit course on MSc Data Science & Product Analytics (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.

GMDPA712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice
GMDPA712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice
GMDPA712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice
GMDPA712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice
GMDPA712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice
GMDPA712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice

3 · 30 h

GMDPA713

Dual-Track Capstone: Product Data to Shipped Decision

Dual-Track Capstone: Product Data to Shipped Decision (GMDPA713) is a 6-credit course on MSc Data Science & Product Analytics (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: Product Data to Shipped 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.

GMDPA713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Product Data to Shipped Decision
GMDPA713-M2 · Features, baselines, and error — Dual-Track Capstone: Product Data to Shipped Decision
GMDPA713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Product Data to Shipped Decision
GMDPA713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Product Data to Shipped Decision
GMDPA713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Product Data to Shipped Decision
GMDPA713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Product Data to Shipped 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 Analytics (Dual Track)?

The published duration is 24 months. Teaching language: English (ES/PT/HI support hubs do not change the language of assessment unless a bilingual pathway is stated). Actual time-to-complete depends on mode and any recognised prior learning.

Can I study this from my country?

This is a fully online award. You study from your country. No student visa and no campus relocation are required.

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. GlobalMind 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@gmuniv.com.

Recognised & Accredited

Ready to begin?MSc Data Science & Product Analytics (Dual Track)

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