Course structure and modules  Individualisation – Your studies, your profile

Fastlinks:
Required Modules | Elective Modules | Master Thesis Project | Programme Information | Contact | Info-Events

Coursework

The MSc in Applied Information and Data Science requires you to earn 120 credits under the European Credit Transfer System (ECTS). At Lucerne University of Applied Sciences and Arts, one ECTS credit corresponds to a workload of 30 hours. This includes lectures, self-study, semester projects and examinations.

Language of instruction

The language of instruction is English, with only a small number of modules taught in German. You may choose to complete the assessment for a module in either English or German, regardless of the language in which the module is taught.

Programme structure

The MSc in Applied Information and Data Science consists of three areas, each outlined below:

• Required Modules
• Elective Modules
• Master's Thesis Project

The required modules guide you step by step through the foundations of applied data science, covering topics ranging from computer science and Python to statistics, machine learning and data management. The modules are designed so that no prior knowledge is required. If you can demonstrate existing knowledge in specific areas, up to 12 ECTS credits may be recognised.

The elective modules provide you with the opportunity to deepen your personal interests, build on your strengths and develop an individual profile. They are organised into four areas:

General Elective Modules (no minimum ECTS required)

The General Elective Modules accompany you throughout the entire Data Science and AI lifecycle: from understanding a problem and preparing data to modelling, evaluation, visualisation and communication of results. You are free to choose the topics you would like to explore in greater depth.

In addition, four external modules are available in collaboration with renowned partners:

• Global School of Empirical Research Methods (GSERM)
• IBM WatsonX GenAI Challenge
• Collaborative Innovation Networks (COINs)
• SAS Joint Certificate

Domain Experience (no minimum ECTS required)

The Domain Experience modules allow you to apply Data Science and AI directly within your preferred industry or application domain. You can choose from a wide range of fields aligned with your career aspirations, including healthcare, finance, energy, sports and sustainability.

Project-based Learning and Work Experience (max. 12 ECTS)

This area connects academic study with professional practice. You may choose one of the following options or combine both:

• Project-based Learning with bydo: Through bydo, you gain hands-on experience by working on real projects in a genuine business environment and can integrate this experience directly into your studies.
• Recognition of work experience: If you are already working in a data-related role, a limited amount of your professional experience may be recognised as part of your degree programme. 

Advanced Analytics and Engineering (min. 12 ECTS)

The Advanced Analytics and Engineering modules allow you to strengthen your technical and analytical profile. Topics include areas such as Deep Learning, Computer Vision, Natural Language Processing and Data Engineering.

In the Master's Thesis Project, you demonstrate that you can undertake real-world data projects with scientific rigour and a high level of professional competence. It consists of two consecutive modules: the Preliminary Study and the Master's Thesis.

Preliminary Study (6 ECTS)

In the Preliminary Study, you establish the conceptual and methodological foundations for your subsequent Master's Thesis. The Preliminary Study is assessed as an independent module.

Master's Thesis (21 ECTS)

The Master's Thesis builds directly on the Preliminary Study. You work on a real-world data project with scientific depth and a strong practical focus. The Master's Thesis is also assessed as an independent module with a separate performance assessment.

Master's thesis projects to date