An advanced academic pathway designed to equip learners and professionals with rigorous scientific, operational, and clinical knowledge.
This qualification is an institutional private academic Bachelor degree awarded under Swiss private educational law (Art. 76 Cantonal School Act Zug). In accordance with Swiss Cantonal and Federal regulations, private degrees are not Swiss state-recognized university degrees and do NOT confer statutory licensure or clinical practice rights in regulated healthcare professions (such as medicine, nursing, dentistry, or pharmacy).
The BSc in Health Data Analytics is engineered to meet the evolving demands of modern health systems. Through a balanced synthesis of empirical inquiry, critical literature appraisal, and practical problem-solving, learners develop deep domain expertise.
Whether focusing on health administration, epidemiology, or specialized clinician knowledge updates, curricula are structured modularly to allow working professionals to master competencies sequentially.
| Code | Course Title | Credits | Format | Course Learning Focus & Outcomes |
|---|---|---|---|---|
| HDA101 | Introduction to Health Data Science | — | Theory | Health data lifecycle, clinical registers and data-driven healthcare transformation |
| HDA102 | Python Programming for Healthcare Analytics | — | Practical | Pandas, NumPy, data cleansing and exploratory health data visualization |
| HDA103 | Foundations of Biostatistics & Probability | — | Practical | Probability distributions, central limit theorem and statistical hypothesis testing |
| HDA104 | Medical Terminology & Clinical Coding (ICD/CPT) | — | Theory | Clinical classification systems, diagnostic grouping and billing informatics |
| HDA105 | Healthcare Systems Architecture & Data Sources | — | Theory | EHRs, LIMS, PACS, claims databases and public health surveillance systems |
| HDA106 | Data Ethics, Patient Confidentiality & Compliance | — | Theory | De-identification standards, GDPR, Swiss FADP and institutional data governance |
| Code | Course Title | Credits | Format | Course Learning Focus & Outcomes |
|---|---|---|---|---|
| HDA111 | Relational Databases & Advanced SQL for Health | — | Practical | Complex joins, window functions and performance tuning for large clinical datasets |
| HDA112 | R Programming for Epidemiological Analytics | — | Practical | Tidyverse data manipulation, ggplot2 graphics and survival curves in R |
| HDA113 | Applied Linear & Logistic Regression in Healthcare | — | Practical | Odds ratios, model diagnostics, multi-collinearity and receiver operating curves |
| HDA114 | Health Data Wrangling & Feature Engineering | — | Practical | Handling missing clinical variables, imputation techniques and cohort selection |
| HDA115 | Healthcare Performance Metrics & KPI Dashboards | — | Practical | Developing executive hospital dashboards in Tableau and PowerBI |
| HDA116 | Clinical Research Informatics & Data Quality | — | Theory | Validation rules, data provenance and clinical data completeness evaluation |
| Code | Course Title | Credits | Format | Course Learning Focus & Outcomes |
|---|---|---|---|---|
| HDA201 | Machine Learning for Health Data I: Supervised Models | — | Practical | Random forests, gradient boosting and SVMs for clinical risk prediction |
| HDA202 | Machine Learning for Health Data II: Unsupervised Models | — | Practical | Clustering patient cohorts, PCA and anomaly detection in claims fraud |
| HDA203 | Epidemiological Analytics & Risk Modeling | — | Practical | Cox proportional hazards, relative risk and propensity score matching |
| HDA204 | Time-Series Analysis in Healthcare Operations | — | Practical | ARIMA and Prophet models for emergency department patient volume forecasting |
| HDA205 | Cloud Analytics Infrastructure (AWS / Azure Health) | — | Practical | S3 data lakes, BigQuery/Snowflake for health and scalable compute pipelines |
| HDA206 | Health Economics Modeling & Cost-Benefit Analytics | — | Theory | Markov decision processes, cost-effectiveness analysis and quality-adjusted life years |
| Code | Course Title | Credits | Format | Course Learning Focus & Outcomes |
|---|---|---|---|---|
| HDA211 | Natural Language Processing for Clinical Text | — | Practical | Rule-based and transformer models for unstructured clinical progress notes |
| HDA212 | Deep Learning Foundations for Biomedical Data | — | Practical | Multi-layer perceptrons, CNNs and RNNs for physiological waveform analysis |
| HDA213 | Geographic Information Systems (GIS) & Spatial Health | — | Practical | Spatial autocorrelation, disease clustering and demographic mapping in QGIS |
| HDA214 | Genomic & Multi-Omics Data Analytics | — | Practical | Variant call formats (VCF), GWAS data analysis and polygenic risk scoring |
| HDA215 | Predictive Modeling for Hospital Readmissions & Sepsis | — | Practical | Early warning score algorithms (NEWS2/MEWS) and clinical alert optimization |
| HDA216 | Clinical Trials Analytics & Survival Analysis | — | Theory | Kaplan-Meier survival estimation, log-rank tests and clinical trial analytics |
| Code | Course Title | Credits | Format | Course Learning Focus & Outcomes |
|---|---|---|---|---|
| HDA301 | Interoperability Standards & FHIR Data Pipelines | — | Practical | Building automated ETL pipelines extracting and transforming HL7/FHIR resources |
| HDA302 | Model Interpretability & Explainable AI in Medicine (XAI) | — | Theory | SHAP and LIME frameworks for clinical diagnostic transparency and physician trust |
| HDA303 | Big Data Engineering & Distributed Computing (PySpark) | — | Practical | Distributed clinical dataset transformation and scalable machine learning on clusters |
| HDA304 | Healthcare Cybersecurity & Data Pipeline Hardening | — | Theory | Role-based access control (RBAC), end-to-end encryption and audit log analytics |
| HDA305 | Applied Research Methodology in Health Analytics | — | Practical | Study design, pre-registration of analytical plans and reproducible science principles |
| HDA306 | Executive Healthcare Storytelling & Data Visualisation | — | Theory | Translating complex analytical models into actionable insights for hospital boards |
| Code | Course Title | Credits | Format | Course Learning Focus & Outcomes |
|---|---|---|---|---|
| HDA311 | Ethical AI, Bias Mitigation & Algorithmic Fairness | — | Theory | Auditing predictive algorithms for demographic disparities and health equity |
| HDA312 | Real-World Evidence (RWE) & Post-Market Analytics | — | Theory | Extracting regulatory-grade real-world evidence from electronic patient records |
| HDA313 | Applied Capstone Project in Health Data Analytics | — | Practical | End-to-end deployment, validation and defense of a predictive clinical data model |
| HDA314 | Emerging Trends: Large Language Models in Clinical Practice | — | Theory | Evaluating clinical safety, hallucination mitigation and integration of clinical LLMs |
| HDA315 | Professional Capstone Portfolio & Technical Examination | — | Practical | Production-grade code repository, model documentation and oral defense |
| HDA316 | Healthcare Data Leadership & Enterprise Governance | — | Theory | Establishing chief data officer (CDO) governance charters in healthcare institutions |
Recognized upper-secondary qualification (Swiss Matura, International Baccalaureate, or foreign equivalent). English proficiency IELTS 6.0+ or equivalent.
Secondary academic preparation in natural or quantitative sciences; commitment to healthcare systems, epidemiology, or health administration.
Authoritative guidance on award status, eligibility, clinical attachments, and Swiss educational governance.
Recognized upper-secondary qualification (Swiss Matura, International Baccalaureate, or foreign equivalent). English proficiency IELTS 6.0+ or equivalent.
Graduates receive the Private Bachelor of Science (BSc) Candidate issued solely by Brookfield School of Health Sciences, Switzerland. It provides structured continuing medical education evidence and international CPD portfolio alignment (110 CPD hours methodology).
Awarded under Swiss private educational law (Art. 76 Cantonal School Act Zug). This is an independent continuing professional development award and is NOT an official Swiss state medical specialist title (FMH/SIWF) or license to practice medicine.
Yes. For eligible candidates, optional 4 to 8-week supervised hospital clinical attachments can be arranged at accredited partner teaching hospitals, providing intensive ward rounds, procedural observation, and preceptor evaluation.