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School of Life Sciences
and Facility Management

CAS in Digital Life Sciences

Discover the potential of digitalisation in the life sciences! In this CAS programme, you will learn how to identify the opportunities of digitalisation and successfully apply your expertise in a digital working environment. Experts like you are in high demand – both now and in the future.

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At a glance

Qualification:

Certificate of Advanced Studies in Digital Life Sciences (12 ECTS)

Start:

29.08.2025 09:00

Duration:

8 months

Costs:

CHF 6'900.00

Location: 

ZHAW Zürich, Building ZL, Lagerstrasse 41, 8004 Zürich  (Show on Google Maps)

Language of instruction:

  • German, English
  • Predominantly in English.

CAS dates: 

Overview of the dates and times of the CAS in Digital Life Sciences. (PDF 108,9 KB)

The dates for the Elective Modules will follow shortly.

Objectives and content

Target audience

Individuals in the life sciences with a university degree who wish to complement their expertise with skills in computational science and AI. Practitioners with comparable professional competence may also be admitted (admission sur dossier).

Objectives

  • Use digitalisation in the life sciences.
  • Apply your expertise effectively in a digital context.
  • Close the continuing education gap for data science in the life sciences.

Content

The CAS Digital Life Sciences consists of five compulsory modules and one compulsory elective module, which are selected from the extensive programme offered by the Institute of Computational Life Sciences (ICLS). 

Module offerings

Compulsory modules: 

Case Studies and Life Science Applications (2 ECTS)Immerse yourself in the world of data science and learn how to overcome challenges in the life sciences with customised data solutions. 

Einführung ins Programmieren mit Python (2 ECTS): Acquire basic programming skills in Python and create your first programmes and data structures. 

Data Analysis Fundamentals (2 ECTS): Master the basics of data analysis with Python and use high-quality models and visualisations for data-driven decisions. 

Machine Learning Fundamentals in Python (2 ECTS): Discover the key techniques of machine learning and develop your own algorithms for data analysis and visualisation with Python. 

Introduction to Neural Networks (2 ECTS): Explore the basis of neural networks and their application potential for predictions in the life sciences. 

Compulsory elective modules: 

Natural Language Processing Fundamentals (2 ECTS): Deepen your programming skills and learn how to automatically process and analyse language data. 

Simulation for Beginners (2 ECTS): Develop models, analyse data and optimise complex systems with theoretical foundations and practical exercises for your professional success. 

Bioinformatics for Beginners (2 ECTS): Discover the basics of bioinformatics from data acquisition to analysis and modelling in molecular biology applications. 

Einführung in SQL (2 ECTS): Immerse yourself in the world of SQL databases and learn how to develop, manage and query databases and optimise data structures. 

Good Reasons for the CAS in Digital Life Sciences

  • Digital transformation: Digitalisation is rapidly changing all areas of life and work.
  • Handling data volumes: The life sciences generate increasingly large data sets that need to be analysed.
  • Skills shortage: There is a high demand for skilled professionals in data science for the life sciences.
  • New access: The CAS offers professionals in the life sciences new access to data science.

Important Information for participants of the old CAS

Students who began their studies before 10 July 2024 will follow the study regulations dated 1 February 2024. Link to the old study regulations.(PDF 222,6 KB)

Study format

  • Part-time, predominantly in English.
  • Duration of Study: 8 months.

Methodology

Our teaching methods are designed to provide you with a practical and in-depth learning experience:

  • Interactive Lectures: Our experts deliver knowledge through dynamic and interactive presentations.
  • Practical Group Exercises: Work in teams on real case studies to directly apply and deepen what you've learned.
  • Self-study with Support: Utilize comprehensive resources and guided learning to work at your own pace and solidify your understanding.
  • Transfer Projects: Apply the learning content to your professional practice and benefit from immediate feedback.
  • Real-world Case Studies: Analyze and discuss actual cases from the life sciences to gain a thorough understanding of digital transformation.

Enquiries and contact

Provider

Application

Admission requirements

With university degree: Recognized university degree and 2 years of professional experience.

Without university degree: Tertiary B degree, 3 years of professional experience, and successful completion of an admissions interview.

Start Application deadline Registration link
29.08.2025 09:00 01.08.2025 Application

Downloads and brochure

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