CAS Data Science in Medicine & Health Data skills for the health sector of tomorrow

This CAS program provides a focused, practice-oriented introduction to data science tailored to the evolving needs of medicine and health. Designed for professionals across clinical, pharmaceutical, insurance, and healthcare technology fields, it equips you with the essential data skills to make informed decisions, improve patient outcomes, and contribute to smarter, more efficient, data-informed healthcare.

The program at a glance

The program offers a structured and comprehensive introduction to the key concepts and techniques required for the analysis and practical use of health data. Over the course of a few months, participants progress from foundational data science skills to advanced applications used in modern healthcare.

This continuing education program covers essential topics such as data gathering, cleaning, and processing for various types of health data, as well as core methods in statistical analysis and predictive modeling. Ethical, regulatory, and legal aspects of handling sensitive patient information are integrated throughout the curriculum.

In addition, the CAS introduces advanced, practice-relevant topics including clinical decision support systems, natural language processing, and the use of large language models for evidence extraction and knowledge synthesis (e.g., OpenEvidence-type applications). Participants also explore applications of computer vision in medical imaging and learn how these techniques are transforming diagnostics and clinical workflows.

The program further provides an overview of emerging technologies, and examines their growing impact on healthcare delivery and innovation.

Learning goals

The program equips participants with the core skills to analyze health data, generate meaningful insights, and support data-informed decision-making in clinical and healthcare environments.

Key learning goals include:

  • Understanding and applying data science methods across diverse healthcare settings
  • Applying Machine Learning, Predictive Modeling, and Generative AI
  • Navigating ethics, regulatory frameworks, and data protection requirements for sensitive patient data
  • Gaining foundational exposure to Natural Language Processing, Large Language Models, and Computer Vision used in modern medical applications 
More information

Content and topics

1. Foundations of Data Science in Healthcare

  • The role and impact of data science illustrated through real case studies
  • Introduction to Python for health data analysis (clinical, imaging, genomic, sensor, and administrative data)

2. Working with Health Data

  • Data collection, cleaning, and preprocessing tailored to medical datasets
  • Understanding data sources, interoperability challenges, and EHR (electronic health records) systems

3. Analytical Methods and Machine Learning

  • Core statistical techniques: hypothesis testing, regression, and uncertainty
  • Machine Learning essentials for healthcare (e.g., Decision Trees, Random Forests)
  • Predictive Modelling and Risk Stratification for patient outcomes

4. Advanced AI Applications

  • Natural Language Processing and Large Language Models (e.g., ChatGPT) for clinical text and literature analysis
  • Introduction to Computer Vision for medical imaging and other Generative AI use cases

5. Communication, Ethics and Regulation

  • Effective data visualization and communication for clinical and non-technical stakeholders
  • Ethical, legal, and regulatory aspects of working with sensitive patient data
  • Privacy, security, and responsible use of AI in healthcare

 

Facts

Start

13 March 2026

End

11 September 2026

Application deadline

2 weeks before the start of the program

Duration

6 months

Course costs

CHF 7900.-

Registration fee and course materials are included. Premium members of HSLU Alumni are entitled to a 5 % discount. SVEB continuing education vouchers are accepted.

Head
Information events
Degree

Certificate of Advanced Studies Hochschule Luzern/FHZ in Data Science in Medicine & Health

Type

CAS

ECTS

15

Tuition times

Friday and Saturday

Language of instruction
  • German
  • English
Venue

Rotkreuz

Contact hours

120 Lessions

Lecturers
Target group

This CAS is designed for professionals across the entire healthcare and life-science ecosystem, including:

  • Clinicians and medical specialists: chief physicians, senior doctors, study nurses, clinical study assistants, and medical staff involved in diagnostics, patient care, or clinical decision-making
  • Hospital and medical center professionals: quality managers, IT leaders, biomedical and medical technicians, and experts driving clinical operations or digital transformation
  • Pharma, biotech, and research experts: project managers, R&D teams, regulated development specialists, and scientific researchers working with data-driven innovation
  • Insurance and health economics professionals: specialists from health insurers, and professionals focused on risk, cost, utilization, or outcome analytics
  • Data, digital health, and technology roles: data scientists, AI strategy leads, and all specialists working at the interface between technology and healthcare
  • Managers and decision-makers across healthcare institutions, insurers, universities, and industry who need to understand and leverage health data for strategic planning and innovation
  • Professionals transitioning into the healthcare domain: individuals from tech, data, or analytics backgrounds seeking to specialize in medicine and health
 
Requirements

A degree at tertiary level (ETH/University, University of Applied Sciences, college of higher education and others) and at least two years of professional experience after graduation. Persons with an equivalent qualification and several years of professional experience may be admitted in limited numbers via a standardized admission procedure ("sur dossier") - this may be subject to conditions.

Provider(s)

Computer Science and Information Technology
Continuing Education

Methodology

This program combines a range of modern learning methods designed to support both practical skill-building and professional growth:

  • Supervised self-study
    Participants receive high-quality learning materials and digital resources that enable flexible, self-paced learning. Participants are supported throughout by dedicated lecturers and tutors who provide guidance, clarification, and expert feedback.
  • Practice-oriented transfer project
    Each participant works on a real-world transfer project, applying newly acquired skills directly to their professional environment and generating tangible value for their organization.
  • Specialization project
    Participants can deepen their expertise in a topic of personal or professional relevance, exploring an area of healthcare data science that aligns with their career goals.
  • Collaborative learning
    Group projects, peer exchange, and guided discussions create a dynamic learning community, encouraging cross-disciplinary insights and collaboration across diverse healthcare and industry backgrounds.
 

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