MSc

MS Data Science & Business Strategy (Dual Track)

MS Data Science & Business Strategy — one admission, a shared core, two named specialisations.

Online or on campus

24 months42 creditsEnglish (Minneapolis campus or tuition-free online; one academic standard)

MS Data Science & Business Strategy (Dual Track)

Overview

A double-path master's: a shared quantitative and strategy core, then two pathways — Data Science (machine learning, deployment, experimentation) and Business Strategy (competitive analysis, growth, execution) — ending with a capstone that turns a model into a business recommendation. Mltio University runs this award on its two honest pathways — online from anywhere, or on campus in Minneapolis — to a single academic standard, with identity-checked assessment on both. 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. MS Data Science & Business Strategy (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; Strategy, Markets & Competitive Analysis; Research Methods & Identity-Checked Assessment; Machine Learning Theory & Practice; Predictive Modeling & Forecasting, and finish on Dual-Track Capstone: Model to Business Recommendation. 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 Mltio University awards. No professional-body logo is used in marketing to imply endorsement.

Who This Program 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
✦ 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 Minneapolis campus? On-campus delivery includes tuition fees, in-person seminars, and access to university facilities.

✦ Tuition: £9,500 / $12,000/year
✦ Location: Minneapolis, MN 55455, USA
✦ 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 Data & Strategy Core

Code	Course	Credits · hours
DSBS701	Statistics & Experimental Design Statistics & Experimental Design (DSBS701) is a 3-credit course on MS Data Science & Business Strategy (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. DSBS701-M1 · The question, the unit of analysis, and data quality — Statistics & Experimental Design DSBS701-M2 · Features, baselines, and error — Statistics & Experimental Design DSBS701-M3 · Validation, leakage, and what the model must not decide — Statistics & Experimental Design DSBS701-M4 · Monitoring, drift, and human review — Statistics & Experimental Design DSBS701-M5 · Interpretation for a non-technical decision maker — Statistics & Experimental Design DSBS701-M6 · A supervised project with method, result, and limitation — Statistics & Experimental Design	3 · 30 h
DSBS702	Data Engineering & Analytics Foundations Data Engineering & Analytics Foundations (DSBS702) is a 3-credit course on MS Data Science & Business Strategy (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. DSBS702-M1 · The question, the unit of analysis, and data quality — Data Engineering & Analytics Foundations DSBS702-M2 · Features, baselines, and error — Data Engineering & Analytics Foundations DSBS702-M3 · Validation, leakage, and what the model must not decide — Data Engineering & Analytics Foundations DSBS702-M4 · Monitoring, drift, and human review — Data Engineering & Analytics Foundations DSBS702-M5 · Interpretation for a non-technical decision maker — Data Engineering & Analytics Foundations DSBS702-M6 · A supervised project with method, result, and limitation — Data Engineering & Analytics Foundations	3 · 30 h
DSBS703	Strategy, Markets & Competitive Analysis Strategy, Markets & Competitive Analysis (DSBS703) is a 3-credit course on MS Data Science & Business Strategy (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 Strategy, Markets & Competitive 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. DSBS703-M1 · The question, the unit of analysis, and data quality — Strategy, Markets & Competitive Analysis DSBS703-M2 · Features, baselines, and error — Strategy, Markets & Competitive Analysis DSBS703-M3 · Validation, leakage, and what the model must not decide — Strategy, Markets & Competitive Analysis DSBS703-M4 · Monitoring, drift, and human review — Strategy, Markets & Competitive Analysis DSBS703-M5 · Interpretation for a non-technical decision maker — Strategy, Markets & Competitive Analysis DSBS703-M6 · A supervised project with method, result, and limitation — Strategy, Markets & Competitive Analysis	3 · 30 h
DSBS704	Research Methods & Identity-Checked Assessment Research Methods & Identity-Checked Assessment (DSBS704) is a 3-credit course on MS Data Science & Business Strategy (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. DSBS704-M1 · The question, the unit of analysis, and data quality — Research Methods & Identity-Checked Assessment DSBS704-M2 · Features, baselines, and error — Research Methods & Identity-Checked Assessment DSBS704-M3 · Validation, leakage, and what the model must not decide — Research Methods & Identity-Checked Assessment DSBS704-M4 · Monitoring, drift, and human review — Research Methods & Identity-Checked Assessment DSBS704-M5 · Interpretation for a non-technical decision maker — Research Methods & Identity-Checked Assessment DSBS704-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
DSBS705	Machine Learning Theory & Practice Machine Learning Theory & Practice (DSBS705) is a 3-credit course on MS Data Science & Business Strategy (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. DSBS705-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice DSBS705-M2 · Features, baselines, and error — Machine Learning Theory & Practice DSBS705-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice DSBS705-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice DSBS705-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice DSBS705-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	3 · 30 h
DSBS706	Predictive Modeling & Forecasting Predictive Modeling & Forecasting (DSBS706) is a 3-credit course on MS Data Science & Business Strategy (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 Modeling & 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. DSBS706-M1 · The question, the unit of analysis, and data quality — Predictive Modeling & Forecasting DSBS706-M2 · Features, baselines, and error — Predictive Modeling & Forecasting DSBS706-M3 · Validation, leakage, and what the model must not decide — Predictive Modeling & Forecasting DSBS706-M4 · Monitoring, drift, and human review — Predictive Modeling & Forecasting DSBS706-M5 · Interpretation for a non-technical decision maker — Predictive Modeling & Forecasting DSBS706-M6 · A supervised project with method, result, and limitation — Predictive Modeling & Forecasting	3 · 30 h
DSBS707	Model Deployment, MLOps & Monitoring Model Deployment, MLOps & Monitoring (DSBS707) is a 3-credit course on MS Data Science & Business Strategy (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 Model Deployment, MLOps & 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. DSBS707-M1 · The question, the unit of analysis, and data quality — Model Deployment, MLOps & Monitoring DSBS707-M2 · Features, baselines, and error — Model Deployment, MLOps & Monitoring DSBS707-M3 · Validation, leakage, and what the model must not decide — Model Deployment, MLOps & Monitoring DSBS707-M4 · Monitoring, drift, and human review — Model Deployment, MLOps & Monitoring DSBS707-M5 · Interpretation for a non-technical decision maker — Model Deployment, MLOps & Monitoring DSBS707-M6 · A supervised project with method, result, and limitation — Model Deployment, MLOps & Monitoring	3 · 30 h
DSBS708	Causal Inference & A/B Testing Causal Inference & A/B Testing (DSBS708) is a 3-credit course on MS Data Science & Business Strategy (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 & A/B Testing as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone. DSBS708-M1 · The question, the unit of analysis, and data quality — Causal Inference & A/B Testing DSBS708-M2 · Features, baselines, and error — Causal Inference & A/B Testing DSBS708-M3 · Validation, leakage, and what the model must not decide — Causal Inference & A/B Testing DSBS708-M4 · Monitoring, drift, and human review — Causal Inference & A/B Testing DSBS708-M5 · Interpretation for a non-technical decision maker — Causal Inference & A/B Testing DSBS708-M6 · A supervised project with method, result, and limitation — Causal Inference & A/B Testing	3 · 30 h

Term 3 — Pathway B: Business Strategy

Code	Course	Credits · hours
DSBS709	Corporate & Business-Unit Strategy Corporate & Business-Unit Strategy (DSBS709) is a 3-credit course on MS Data Science & Business Strategy (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 Corporate & Business-Unit Strategy 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. DSBS709-M1 · The question, the unit of analysis, and data quality — Corporate & Business-Unit Strategy DSBS709-M2 · Features, baselines, and error — Corporate & Business-Unit Strategy DSBS709-M3 · Validation, leakage, and what the model must not decide — Corporate & Business-Unit Strategy DSBS709-M4 · Monitoring, drift, and human review — Corporate & Business-Unit Strategy DSBS709-M5 · Interpretation for a non-technical decision maker — Corporate & Business-Unit Strategy DSBS709-M6 · A supervised project with method, result, and limitation — Corporate & Business-Unit Strategy	3 · 30 h
DSBS710	Growth, Pricing & Go-to-Market Growth, Pricing & Go-to-Market (DSBS710) is a 3-credit course on MS Data Science & Business Strategy (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 Growth, Pricing & 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. DSBS710-M1 · The question, the unit of analysis, and data quality — Growth, Pricing & Go-to-Market DSBS710-M2 · Features, baselines, and error — Growth, Pricing & Go-to-Market DSBS710-M3 · Validation, leakage, and what the model must not decide — Growth, Pricing & Go-to-Market DSBS710-M4 · Monitoring, drift, and human review — Growth, Pricing & Go-to-Market DSBS710-M5 · Interpretation for a non-technical decision maker — Growth, Pricing & Go-to-Market DSBS710-M6 · A supervised project with method, result, and limitation — Growth, Pricing & Go-to-Market	3 · 30 h
DSBS711	Strategy Execution & Performance Systems Strategy Execution & Performance Systems (DSBS711) is a 3-credit course on MS Data Science & Business Strategy (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 Strategy Execution & Performance Systems 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. DSBS711-M1 · The question, the unit of analysis, and data quality — Strategy Execution & Performance Systems DSBS711-M2 · Features, baselines, and error — Strategy Execution & Performance Systems DSBS711-M3 · Validation, leakage, and what the model must not decide — Strategy Execution & Performance Systems DSBS711-M4 · Monitoring, drift, and human review — Strategy Execution & Performance Systems DSBS711-M5 · Interpretation for a non-technical decision maker — Strategy Execution & Performance Systems DSBS711-M6 · A supervised project with method, result, and limitation — Strategy Execution & Performance Systems	3 · 30 h

Code

Course

Credits · hours

DSBS709

Corporate & Business-Unit Strategy

Corporate & Business-Unit Strategy (DSBS709) is a 3-credit course on MS Data Science & Business Strategy (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 Corporate & Business-Unit Strategy 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.

DSBS709-M1 · The question, the unit of analysis, and data quality — Corporate & Business-Unit Strategy
DSBS709-M2 · Features, baselines, and error — Corporate & Business-Unit Strategy
DSBS709-M3 · Validation, leakage, and what the model must not decide — Corporate & Business-Unit Strategy
DSBS709-M4 · Monitoring, drift, and human review — Corporate & Business-Unit Strategy
DSBS709-M5 · Interpretation for a non-technical decision maker — Corporate & Business-Unit Strategy
DSBS709-M6 · A supervised project with method, result, and limitation — Corporate & Business-Unit Strategy

3 · 30 h

DSBS710

Growth, Pricing & Go-to-Market

Growth, Pricing & Go-to-Market (DSBS710) is a 3-credit course on MS Data Science & Business Strategy (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 Growth, Pricing & 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.

DSBS710-M1 · The question, the unit of analysis, and data quality — Growth, Pricing & Go-to-Market
DSBS710-M2 · Features, baselines, and error — Growth, Pricing & Go-to-Market
DSBS710-M3 · Validation, leakage, and what the model must not decide — Growth, Pricing & Go-to-Market
DSBS710-M4 · Monitoring, drift, and human review — Growth, Pricing & Go-to-Market
DSBS710-M5 · Interpretation for a non-technical decision maker — Growth, Pricing & Go-to-Market
DSBS710-M6 · A supervised project with method, result, and limitation — Growth, Pricing & Go-to-Market

3 · 30 h

DSBS711

Strategy Execution & Performance Systems

Strategy Execution & Performance Systems (DSBS711) is a 3-credit course on MS Data Science & Business Strategy (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 Strategy Execution & Performance Systems 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.

DSBS711-M1 · The question, the unit of analysis, and data quality — Strategy Execution & Performance Systems
DSBS711-M2 · Features, baselines, and error — Strategy Execution & Performance Systems
DSBS711-M3 · Validation, leakage, and what the model must not decide — Strategy Execution & Performance Systems
DSBS711-M4 · Monitoring, drift, and human review — Strategy Execution & Performance Systems
DSBS711-M5 · Interpretation for a non-technical decision maker — Strategy Execution & Performance Systems
DSBS711-M6 · A supervised project with method, result, and limitation — Strategy Execution & Performance Systems

3 · 30 h

Term 4 — Integration & Dual-Track Capstone

Code	Course	Credits · hours
DSBS712	Integrating Two Disciplines in Practice Integrating Two Disciplines in Practice (DSBS712) is a 3-credit course on MS Data Science & Business Strategy (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. DSBS712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice DSBS712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice DSBS712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice DSBS712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice DSBS712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice DSBS712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice	3 · 30 h
DSBS713	Dual-Track Capstone: Model to Business Recommendation Dual-Track Capstone: Model to Business Recommendation (DSBS713) is a 6-credit course on MS Data Science & Business Strategy (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: Model to Business Recommendation 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. DSBS713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Model to Business Recommendation DSBS713-M2 · Features, baselines, and error — Dual-Track Capstone: Model to Business Recommendation DSBS713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Model to Business Recommendation DSBS713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Model to Business Recommendation DSBS713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Model to Business Recommendation DSBS713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Model to Business Recommendation	6 · 60 h

Code

Course

Credits · hours

DSBS712

Integrating Two Disciplines in Practice

Integrating Two Disciplines in Practice (DSBS712) is a 3-credit course on MS Data Science & Business Strategy (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.

DSBS712-M1 · The question, the unit of analysis, and data quality — Integrating Two Disciplines in Practice
DSBS712-M2 · Features, baselines, and error — Integrating Two Disciplines in Practice
DSBS712-M3 · Validation, leakage, and what the model must not decide — Integrating Two Disciplines in Practice
DSBS712-M4 · Monitoring, drift, and human review — Integrating Two Disciplines in Practice
DSBS712-M5 · Interpretation for a non-technical decision maker — Integrating Two Disciplines in Practice
DSBS712-M6 · A supervised project with method, result, and limitation — Integrating Two Disciplines in Practice

3 · 30 h

DSBS713

Dual-Track Capstone: Model to Business Recommendation

Dual-Track Capstone: Model to Business Recommendation (DSBS713) is a 6-credit course on MS Data Science & Business Strategy (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: Model to Business Recommendation 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.

DSBS713-M1 · The question, the unit of analysis, and data quality — Dual-Track Capstone: Model to Business Recommendation
DSBS713-M2 · Features, baselines, and error — Dual-Track Capstone: Model to Business Recommendation
DSBS713-M3 · Validation, leakage, and what the model must not decide — Dual-Track Capstone: Model to Business Recommendation
DSBS713-M4 · Monitoring, drift, and human review — Dual-Track Capstone: Model to Business Recommendation
DSBS713-M5 · Interpretation for a non-technical decision maker — Dual-Track Capstone: Model to Business Recommendation
DSBS713-M6 · A supervised project with method, result, and limitation — Dual-Track Capstone: Model to Business Recommendation

6 · 60 h

Career Paths

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

Program 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 MS Data Science & Business Strategy (Dual Track)?

The published duration is 24 months. Teaching language: English (Minneapolis 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 Mltio. 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. Mltio 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@mltio.com.

Recognized & Accredited

Program Details

Duration: 24 months
Credits: 42
Language: English (Minneapolis campus or tuition-free online; one academic standard)
Study modes: Online or on campus

Apply online (free)Apply for campus

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

On-campus study

Study at our campus

Prefer to learn at our Minneapolis campus? On-campus delivery includes tuition fees, in-person seminars, and access to university facilities.

✦ Tuition: £9,500 / $12,000/year
✦ Location: Minneapolis, MN 55455, USA
✦ Campus examinations are charged
✦ In-person seminars, library & student services

Questions?

Our admissions team can help you choose the right program and prepare your application.

Contact Admissions

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