Spring 2025

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

This CAS program offers a comprehensive and structured introduction to data science tailored to the needs and challenges of the medicine and health sectors.

Spring 2025

The program at a glance

The program offers an in-depth introduction to the key concepts and techniques required for the analysis and utilization of health data. This continuing education program delves into various aspects of data processing, statistical analysis, machine learning and more, with special focus on medical practice.

It covers the key basics from an introduction to the role of data science in the health sector, data gathering and processing the different types of data and sources. Participants gain insight into the methods of statistical analysis and predictive modeling techniques. The ethical implications of handling patient information are also part of the program.

The CAS program also discusses cutting-edge topics such as the development of clinical decision support systems, using natural language processing in the health sector and conducting real-world data studies. Furthermore, we provide an overview of new technologies in the fields of artificial intelligence and the internet of things and their potential effects.

Learning goals

The program provides participants with the necessary skills and knowledge to make data-driven decisions in healthcare. They will learn to analyze complex medical data to allow for informed clinical decision-making and improve the quality of patient care. Key topics include:

  • Understanding and applying data science in healthcare 
  • Effective visualization and targeted communication of data 
  • Application of machine learning and predictive modeling 
  • Ethics and data protection in the handling of patient data
  • Integration and analysis of different types of health data
More information

Content and topics

  1. Introduction: The role and importance of data science in healthcare shown through case studies and success stories
  2. Introduction to Python
  3. Fundamentals of data processing and data types and sources: data collection, data cleansing and pre-processing techniques specific to health data. Different types of health data (clinical, genomic, imaging data, etc.) and their sources.
  4. Statistical analysis and machine learning: Fundamentals of statistical methods for analyzing health data, including hypothesis testing and regression analysis. Introduction to machine learning focusing on algorithms commonly used in healthcare (e.g., decision trees, random forests).
  5. Data visualization and communication: Techniques for effectively visualizing and communicating data results to non-specialist stakeholders.
  6. Predictive modelling and risk stratification: Methods of developing models to predict patient outcomes and stratify patients based on risk. 
  7. Natural language processing (NLP): Using NLP to extract meaningful information from clinical notes and medical literature, including ChatGPT. 
  8. Ethics, privacy, and data security: Ethical considerations, privacy laws, and data security measures critical to handling patient data. 
  9. Electronic health records (EHR) and data integration: EHR systems, interoperability issues, ways of integrating different data sources. 
  10. Clinical decision support systems: Design and implementation of systems that use data to support clinical decision-making. 

Facts

Start

Spring 2025 (commences between 21 March 2025 and 11 April 2025)

End

September 2025

Application deadline

2 weeks before the start of the program

Duration

24 days

Course costs

7900.-

Registration fee and course materials are included. 5 percent discount is granted to Premium members of Lucerne University of Applied Sciences and Arts Alumni. SVEB continuing education vouchers are accepted

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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
  • Doctors, nurses, medical researchers and healthcare managers
  • People whose current or future jobs involve analyzing and interpreting health data
  • Medical staff working with clinical decision processes and patient management
  • Specialists at the intersection of technology and healthcare provision will find this program useful because they will learn to use the latest data analysis tools effectively
  • Study nurses, who play a key role in the planning, preparation and delivery of clinical and scientific studies and closely collaborate with study directors, researchers and participants alike
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 applies varied methodologies that build upon conventional classroom teaching:

Supervised self-study: Our participants receive high-quality learning materials and resources that allow them to learn at their own pace. Highly qualified tutors are available to discuss questions and provide guidance and support.

Transfer project: Practice-oriented transfer projects allow participants to directly apply the skills and knowledge acquired in their work environment. 

Specialization projects: Participants have the chance to specialize in thematic areas of relevance to their professional development.

Collaborative learning: Through group projects and group discussions, we foster collaboration among, and the exchange of ideas between, participants.

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Applied Data Intelligence supports Women in Data Science (WiDS). The conference brings together experts and advocates for education and training in data science.