MWMiddle West UniversityAppleton, Wisconsin · USA

MSc — Master of Science in Health Economics and Data Analytics

A quantitative master's degree for professionals who must justify health care decisions with evidence, data and economic reasoning.

Degree awarded
Master of Science (MSc)
Program length
4 semesters
Credits
120 credits
Language
English
OverviewCurriculumPDF downloadsVideos & podcastsResource LibraryMCP resourcesAssessmentThesisTuitionFAQ

About the program

Program overview

The Master of Science is the analytical degree of Middle West University. Where the MBA trains managers and the LL.M trains lawyers, the MSc trains the people who produce the numbers both of them rely on: cost models, outcome studies, reimbursement analyses and evaluations of new interventions.

The program combines health economics with applied data analysis. Students work with anonymised claims, registry and operational datasets in every semester, and each course ends with a deliverable that could be presented to a management board or a payer.

Teaching is fully online. Analytics work is done in spreadsheet and open-source statistical environments, so no licensed commercial software is required.

Mode of study

Fully online, asynchronous study with scheduled tutorial consultations and analytics labs

Admission requirements

Bachelor's degree in economics, business, health sciences, law or a related field; basic numeracy is assumed

Learning outcomes

What graduates are able to do

  1. 01Build and defend a cost-effectiveness model including sensitivity and uncertainty analysis
  2. 02Clean, link and analyse administrative health data while respecting data-protection constraints
  3. 03Interpret and critique published economic evaluations and health technology assessments
  4. 04Design an outcome measurement framework for a clinical or operational service line
  5. 05Communicate quantitative findings to non-technical decision makers in writing and on slides

Full syllabus

Curriculum, semester by semester

Semester 1 — Economic and statistical foundations

The analytical toolkit

30 credits
MSC-101

Health Economics

10 credits

250 hours total workload

Demand and supply for health services, insurance and moral hazard, provider payment systems, market failure and the economics of regulation.

Course learning outcomes
  • Explain why health care markets deviate from competitive assumptions
  • Compare fee-for-service, capitation and DRG payment and their incentive effects
  • Assess the welfare consequences of a proposed reimbursement change

Assessment: Policy analysis paper (60%) and examination (40%)

MSC-102

Applied Statistics and Regression

10 credits

250 hours total workload

Estimation and inference, linear and logistic regression, model diagnostics, confounding and the interpretation of effect sizes.

Course learning outcomes
  • Specify and estimate a regression model appropriate to a research question
  • Diagnose violations of model assumptions and respond to them
  • Report results with confidence intervals and honest interpretation

Assessment: Three analytical problem sets (50%) and examination (50%)

MSC-103

Health Data Management and Governance

10 credits

250 hours total workload

Data structures in claims, EHR and registry systems, linkage and de-identification, quality assessment, and the HIPAA and GDPR constraints on analytical work.

Course learning outcomes
  • Assess the fitness for purpose of an administrative dataset
  • Design a de-identification and access plan for a secondary-use project
  • Document a reproducible data-preparation pipeline

Assessment: Data governance dossier (100%)

Semester 2 — Evaluation methods

Measuring value and outcome

30 credits
MSC-201

Economic Evaluation and Cost-Effectiveness Modelling

12 credits

300 hours total workload

Cost-minimisation, cost-effectiveness, cost-utility and cost-benefit analysis; decision trees, Markov models, discounting and probabilistic sensitivity analysis.

Course learning outcomes
  • Construct a Markov cost-utility model from published inputs
  • Run and present a probabilistic sensitivity analysis
  • Judge whether an evaluation supports its stated conclusion

Assessment: Full model with technical report (70%) and defence (30%)

MSC-202

Outcomes Research and Quality Measurement

9 credits

225 hours total workload

Clinical and patient-reported outcome measures, risk adjustment, case-mix, indicator design and the pitfalls of public performance reporting.

Course learning outcomes
  • Select and justify outcome measures for a defined service line
  • Apply risk adjustment and explain its limits
  • Design an indicator set that resists gaming

Assessment: Indicator framework (60%) and critique paper (40%)

MSC-203

Causal Inference with Observational Data

9 credits

225 hours total workload

Matching, difference-in-differences, instrumental variables and interrupted time series applied to policy and programme evaluation.

Course learning outcomes
  • Choose an identification strategy suited to the available data
  • Implement a difference-in-differences analysis and test its assumptions
  • State the causal claim a design does and does not support

Assessment: Empirical replication project (100%)

Semester 3 — Applied analytics

From analysis to decision

30 credits
MSC-301

Predictive Modelling in Health Care

10 credits

250 hours total workload

Prediction versus explanation, validation and calibration, class imbalance, fairness auditing and the governance of algorithmic tools in clinical settings.

Course learning outcomes
  • Develop and validate a risk-prediction model
  • Audit a model for calibration drift and subgroup performance
  • Document a model card suitable for a governance committee

Assessment: Model build with validation report (100%)

MSC-302

Pricing, Reimbursement and Market Access

10 credits

250 hours total workload

Value-based pricing, HTA submissions, managed-entry agreements and negotiation with payers in the United States and Europe.

Course learning outcomes
  • Assemble the economic core of an HTA submission
  • Design a managed-entry agreement addressing payer uncertainty
  • Anticipate the objections a reimbursement committee will raise

Assessment: Submission dossier (70%) and negotiation simulation (30%)

MSC-303

Research Design and Dissertation Preparation

10 credits

250 hours total workload

Question formulation, protocol writing, feasibility assessment, ethics review and pre-registration of the master's research project.

Course learning outcomes
  • Write a complete, feasible research protocol
  • Justify sample, method and analysis plan in advance
  • Pass internal ethics and data-access review

Assessment: Approved protocol (100%)

Semester 4 — Master's dissertation

Independent quantitative research

30 credits
MSC-401

Master's Dissertation and Defence

30 credits

750 hours total workload

An independent 15,000–18,000 word empirical study with reproducible analysis code, supervised by a member of faculty.

Course learning outcomes
  • Execute a pre-registered analysis and report deviations transparently
  • Deliver a reproducible analysis package alongside the written work
  • Defend method and interpretation before an examination panel

Assessment: Dissertation (80%) and oral defence (20%)

PDF downloads

Three English documents for this program

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Videos & podcasts

Recorded teaching you can watch and listen to

Sample clip

A look inside a recorded lecture

A short preview of the recording style used across the MSc. The full library holds 12 video lectures — one masterclass per course plus thesis, assessment and career sessions — released to enrolled students in the study portal.

Podcast series

MSc Study Sessions

10 audio episodes in English, downloadable for offline listening on the commute — course conversations, thesis guidance and student and alumni panels.

  1. Ep. 1 · Welcome to the Master of Science in Health Economics and Data Analytics26 min
  2. Ep. 2 · How to read academic sources fast31 min
  3. Ep. 3 · MSC-101 in conversation: Health Economics43 min
  4. Ep. 4 · MSC-102 in conversation: Applied Statistics and Regression26 min
  5. Ep. 5 · MSC-103 in conversation: Health Data Management and Governance33 min
  6. Ep. 6 · MSC-201 in conversation: Economic Evaluation and Cost-Effectiveness Modelling34 min
  7. Ep. 7 · MSC-202 in conversation: Outcomes Research and Quality Measurement41 min
  8. Ep. 8 · Writing the thesis proposal39 min
  9. Ep. 9 · Study, work and family: three students talk42 min
  10. Ep. 10 · Life after the MSc35 min
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Resource Library

Presentations, templates and study resources

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MCP resources

Study material for AI assistants

The MSc study material is also published as a machine-readable catalogue, so an AI assistant can read the curriculum, materials, lectures and podcasts directly. Add the endpoint below to any assistant that supports the Model Context Protocol.

Endpointhttps://middlewestuniversity.com/mcp

  • Program profile

    Overview, target audience, learning outcomes, thesis rules, faculty and FAQ.

    get_program { "slug": "msc" }

  • Curriculum

    Every semester and course with credits, workload, outcomes and assessment.

    get_curriculum { "slug": "msc" }

  • Study materials

    Downloadable handbooks and syllabi, readings and slide dossiers.

    list_resources { "slug": "msc", "kind": "pdf" }

  • Videos and podcasts

    Recorded lectures and podcast episodes with durations, hosts and chapters.

    list_media { "slug": "msc", "kind": "podcast" }

  • Course search

    Keyword search across the courses of all seven programs.

    search_courses { "query": "financial reporting" }

  • Tuition

    Published tuition, instalment plan and what the fee covers.

    get_tuition {}

How students are graded

Assessment structure

50–70%

Analytical projects

Models, datasets and code submitted with a written technical report.

20–40%

Examinations

Open-book examinations testing method selection and interpretation.

10–30%

Defences and simulations

Recorded defence of models and a live payer negotiation simulation.

30 credits

Dissertation

Empirical study with reproducible analysis package and oral defence.

Final independent work

Master's dissertation (30 credits)

The MSc dissertation is an empirical study of 15,000–18,000 words accompanied by a reproducible analysis package. Topics must address a health economic, outcome or data-governance question and are approved at the end of semester three.

  1. Semester 3: protocol, data-access approval and pre-registration
  2. Weeks 1–4: data preparation and cohort construction
  3. Weeks 5–10: primary analysis and robustness checks
  4. Weeks 11–16: full draft, supervisor feedback and code review
  5. Weeks 17–18: submission, similarity check and oral defence

Fees

Tuition and payment

Total tuition

USD USD 4,999

Total tuition for the complete Master of Science, including datasets, analytical templates, tutorial consultations, examinations and the dissertation defence.

  • Deposit on enrolment: USD 999
  • Four semester instalments of USD 1,000
  • No separate examination, material or graduation fees

Tuition is quoted in United States dollars. Payment schedules are agreed in writing with the Student Affairs Office before enrolment.

Questions

Frequently asked questions

Do I need programming experience?

No. The program starts with spreadsheet-based analysis and introduces open-source statistical tools step by step from the first semester.

Is the MSc more technical than the MBA?

Yes. The MSc is built around quantitative methods and evaluation; the MBA is built around management and strategy.

Which software do I need to buy?

None. All required tools are free or open source and installation guides are provided.

Can I use my employer's data for the dissertation?

Yes, subject to a data-access agreement and internal ethics approval, which the supervisor helps you obtain.

Interested in the MSc?

The Student Affairs Office answers curriculum, admission and enrolment questions in English.

Request information

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