Data Science

A Gateway to Data-Driven Innovation and Discovery in Molecular Life Sciences

Key data

Degree: Master of Science in Life Sciences
Study mode: Double-Degree with foreign University optional
ECTS points: 90
Studying mode: Full-time and part-time
School days: Monday to Friday
Teaching language: English
Place: Muttenz (Basel-Land)
Stay abroad: optional
Application fee: CHF 200

The deadline for applications for the autumn semester is 30 April of the relevant year and 30 November for the spring semester.

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New specialisation starting Autumn Semester 2024

At the intersection of cutting edge scientific innovation and industry demand, our Data Science specialisation within the MSc in Life Sciences programme trains students for current and future opportunities in the data-driven life science sector.

Data Science – Career Opportunities
The Data Science specialisation is designed to prepare you for a career in industrial, research, academic and clinical environments. The programme recognizes the growing global significance of data science in the analysis and interpretation of complex biological, chemical and pharmacological data. With the advent of high-throughput technologies and the vast quantities of data they produce, data science has become indispensable to the research and development of new drugs, molecular diagnostics and chemical analysis.

Data Science – Curriculum
The comprehensive curriculum provides extensive IT skills training centred around basic and advanced programming, algorithms, databases and deep learning. The flexibility to pursue specialized electives enables a focus on areas of personal or professional interest. Whether deciphering the language of genes, predicting disease pathways or influencing the next wave of precision medicine, the skills you acquire here will qualify you for key roles in research labs, innovative startups and industry giants.
An integral component of the Data Science specialisation is the eight-month research thesis: an opportunity to engage with practical challenges in an industrial or academic environment. The MSc thesis project is an excellent platform for professional development.

Data Science – Entry Requirements
The MSc in Life Sciences – Data Science is especially suited for students with a BSc in natural science or life sciences who have a strong inclination for mathematics and IT.
For full-time students a start in the autumn semester is highly recommended.

Modul Groups for Data Science

Module Group Data Science (6 out of 7 required)

Computer and Software Architectures
Programming, Algorithms and Data Structure
Data Bases
Process Modelling and Simulations
Deep Learning
Human Machine Interaction and Bias Mitigation
Artificial Intelligence in Drug Discovery

Module Group Electives (2 out of 8 required)

Genomics
Biomarker
Proteomics and Protein Analytics
Advanced Mass Spectrometry
Molecular & Translational Imaging
Process Automation
Laboratory Automation in the Pharmaceutical Industry
Process Analytical Technology

Module Group Cluster-Specific (3 out of 5 required)

Modelling of Complex Systems
Machine Learning and Pattern Recognition
Optimisation and Bioinspired Algorithms
Imaging for the Life Sciences
Foodomics

Module Group Core Competences (4 out of 8 required)

Handling and Visualizing data
Design and Analysis of Experiments
Modelling and Exploration of Multivariate Data
Data and Ethics
Business Administration for Life Sciences
Management and Leadership for Life Sciences
Innovation and Project Management
Politics and Society

N.B. In total 50 ECTS (meaning 17 modules a 3 ECTS) have to be gained. Further modules can be chosen: Module Overview
You may also plan your studies in advance with our MSc Life Sciences planning tool.

Admission

Graduates with an excellent Bachelor's degree are admitted directly if they:

hold a Bachelor's degree in a related technical field from a university of applied sciences with a mark of A, B or ≥ 5 or an equivalent mark (≥ good)
or demonstrate an equivalent background (BSc) and practical experience
and have excellent English skills.

Prospective students who meet most but not all of the entry requirements are invited to an interview.