FAQ – Frequently asked questions Master of Science in Applied Information and Data Science

During the programme, you will learn about the potential that data and artificial intelligence (AI) harbour in the context of business and society, as well as about their specific applications.

You will also learn how to analyse large data volumes, arrive at meaningful findings and communicate them clearly. At the same time, you will learn how to design data-driven solutions and apply them for a specific purpose.

The programme particularly focuses on:
• Understanding how technology, analysis and management connect
• Developing data-driven products and business models, implementing them technically, and then integrating them into a business environment
• Using data and AI responsibly

Learn more about our students' specific projects and research in their testimonials here.

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The term "applied" here means keeping a consistent focus on how a technology will actually be used.

You will learn not only about the methods and technologies but also how to apply them directly in real-life projects, with the aim of developing data solutions that directly add value for a specific purpose.

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The programme is part of the Business School because data science generally addresses questions relating to business decisions and issues concerning society.
The focus is not on studying data per se but on understanding its role when making decisions, developing business models and managing organisations.

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Data science primarily means analysing and interpreting data in order to identify patterns and make predictions.

Artificial intelligence takes this a step further by including systems that independently make decisions or interact with their environment. Furthermore, machine learning forms an important link between data science and artificial intelligence, both of which the programme covers in great detail.

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AI is an integral part of the programme. You will learn about the methods used in connection with AI and how to apply them appropriately and reflect on them critically.

The programme not only covers ways of implementing AI technically but also teaches you how to apply it appropriately within organisations and when making decisions.

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The programme teaches you about the technologies and tools used most frequently in data analysis and data science today.

Depending on the module, this includes programming languages, analysis tools, visualisation programmes, as well as platforms for processing and managing data.

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The topics for Master's theses are often developed in collaboration with companies or in response to specific questions taken from a wide range of industrial contexts.

The topics range from data-driven analyses to model development, all the way to strategy questions relating to data and AI. Students also work on topics relevant to society and public policy (e.g. sustainability, sport, culture).

Learn more about our testimonials and discussions on Master's theses here.

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Project-based learning is an important programme component for which you can earn ECTS credits. You will tackle real-life problems provided by companies and organisations and work directly on-site with their employees and your fellow students.

The projects are carried out in teams and supported by lecturers acting as learning coaches. You will thus learn to combine theory with practice directly and at the same time build your own project portfolio.

Project-based learning takes place in conjunction with our partner bydo. 

Learn more about project-based learning here.

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Participation is a part of your studies, and information about the available projects and their organisers is provided during the programme.

The programme coordinators will gladly help you with making a choice and answer questions concerning credits.

Learn more about project-based learning here.

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The Master in Applied Information and Data Science programme has students from very different backgrounds – all of whom share a strong interest in data-driven topics.

Students typically

• are around 29 years old on average when they start, 
• have approximately 5–7 years of professional experience and
• work while studying (around 95% of students are employed).

The student body is international and includes people from a wide range of disciplines.

Learn more about the different profiles from the testimonials here.

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Career changers are individuals who do not have a typical background in computer science, statistics or data science. The programme is deliberately aimed at students from a range of disciplines – including business, social sciences, engineering and the humanities.

The programme offers them the opportunity to transition into a new professional field and supports them in systematically building new skills for working in a data-driven environment.

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The programme is structured so that students without in-depth prior knowledge can succeed right from the start, and it deliberately accommodates their differences at the entry level.

Students need to be willing to actively tackle new topics, such as programming or statistics, especially at the beginning. Experience has shown that motivated and interested career changers generally settle in well and successfully develop into data scientists.

Learn more about the projects and profiles of career changers on our page with interviews, research projects and findings.

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You will receive specific support as you get started. The programme has integrated foundation courses that systematically fill in possible knowledge you may have.

In addition, you benefit from:

• Preparatory material for an easy start
• Personal support from lecturers
• Close exchange with fellow students
• Clearly structured course materials
• Hands-on projects in which you apply what you've learned directly

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The programme considers students to specialists if they already have solid knowledge in computer science, statistics, data analytics and similar disciplines.

Such students often have practical experience in working with data and wish to develop their skills further in a specific direction.

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The programme is deliberately designed to be challenging for specialist, in that it not only deepens their knowledge but also prepares them for the next career step.

They will build their profile beyond purely technical aspects and learn to assume demanding specialist and leadership roles – like other graduates, who work in senior, lead, or strategic positions.

The focus is particularly on:

• Applying data science and AI in a business context 
• Understanding how technology, business and communication connect
• Working on complex real-life problems 
• Developing your own project portfolio 

Learn more about the projects and profiles of people who have changed careers on our page featuring interviews, research projects and findings.

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Having a Bachelor's degree is a prerequisite. In addition, analytical thinking, an interest in data, and the willingness to engage with new methods and technologies are particularly important.

Learn more about the detailed admission requirements here. 

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You will find the application deadlines on our website. We recommend that you apply early, as the application process involves several steps.

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The application process consists of the following steps:

1. Online application
2. Self-assessment
3. English language test
4. Personal interview if required 

All steps aim to gauge your background accurately and give you the best possible start in the programme.

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The self-assessment gauges your basic ability to analyse and apply methods and helps you and the programme coordinators to understand where you currently stand.

It does not focus on subject-specific knowledge, but rather tests logical and analytical skills that are important for succeeding in the programme. No preparation is required and no aids or sample tests are provided.

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The English test ensures that you have the required language level to do the coursework (entry level according to the European Framework is C1, with conditions B2). It may include different formats such as reading comprehension, writing tasks, or multiple-choice questions.

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An interview is required if learning more about your profile makes sense – for example, when your prior education or professional background is unclear.

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No. Our students have a wide range of life and professional experiences, which actively contribute to the quality of the learning environment.

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There are no prerequisites for specific programming languages. Although having a basic knowledge is helpful, you can also develop that during the programme; for example, within compulsory modules (statistics, computer science) in the first semester.

Learn more about the modules here.

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Professional experience is of advantage but not mandatory. Many students have several years of experience in a particular field beyond their studies, which enriches the learning experience.

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Data science and AI are interdisciplinary topics that are becoming increasingly important across many disciplines and professions, and the Master's programme therefore has an open access policy. Students thus have a wide range of level skills in IT and statistics, and the programme is geared to the entry skills typically found in graduates with a Bachelor's degree in business administration from a Swiss university or university of applied sciences. Students without relevant prior knowledge must be willing to acquire the basics during the first semester, and corresponding offers are integrated into the programme.

Learn more about the modules here.

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The Master in Applied Information and Data Science programme promotes immersive learning in the fields where data scientists usually work. Students may thus be eligible for a limited number of credits for work they have done previously in a data-related professional field if they can document that such work is sufficiently related to the content of the course. The programme coordinators decide on which professional experience will be recognised.

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Previous coursework completed in specialised Bachelor programmes can be recognised (max. 12 ECTS) in the compulsory modules on request. Recognition of previous coursework completed in Master's programmes is also possible, but limited to the compulsory subjects of the programme.

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The programme enables students to attend modules with external partners (e.g. Global School of Empirical Research Methods (GSERM) at the University of St. Gallen, specific modules at the University of Lucerne, certificate courses at SAS, etc.). Other offers are currently under review.

Such coursework can be recognised as part of elective modules. If you have questions, we recommend that you contact the programme coordinators in advance about having coursework recognised.

Learn more about all the modules on this page.

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The programme is taught by lecturers who work at the university and in the applied fields, most of whom have extensive industry experience, in addition to their academic qualifications.

You will thus benefit from a combined effect of a solid theoretical foundation and direct relevance to the applied fields. The course contents address the challenges that companies, organisations and societies face today.

Learn more about all lecturers here. 

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The programme administrators will gladly help you with practical questions relating to your studies.

• Phone: +41 41 228 41 30
• Email: master.ids@hslu.ch

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You can expect to receive personal and straightforward support during the programme. You will benefit from direct contact with your lecturers and from an active student community.

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The programme includes certain preparatory modules in which students learn the basics of computer science and statistics. Those who can document having a background in these areas can have some of their prior work converted into credits to be applied towards their degree. In contrast to other programmes, our Master's course does not require previous academic work in these disciplines and is basically open to anyone with a background in the social sciences, natural sciences and humanities. It also places a strong emphasis on the Master's Thesis, which can be combined easily with a professional activity or an internship.

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There is no hard and fast rule here. You have a lot of freedom in scheduling your daily study routine, but you must reserve two days a week for classes. This means you can freely schedule your work and leisure time, leaving you with enough time to work while studying. We recommend that you plan your workload during semester carefully to ensure there is enough time for your studies and for relaxation. Don’t hesitate to contact the programme head if you have questions or need advice.

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Prolonging the total period of your studies will reduce the workload per semester proportionally. However, you will still need to attend classes on two days, although you will have longer gaps in between.

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The Master’s programme promotes learning that immerses students in the applied world of data science. This means you are eligible for a certain number of credits for activities in a data-related field relevant for your coursework – provided you can demonstrate that they are sufficiently relevant. The decision of whether to grant such credit rests with the programme head.

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The programme allows you to attend modules offered by external partners (e.g. Global School of Empirical Research Methods (GSERM) at the University of St. Gallen, some modules at the University of Lucerne, certificate courses at SAS, etc.). Other offers are being considered as well. Attendance of such courses can help you to meet the requirement in your core electives. Please contact the programme head in advance if you have any questions in this connection.

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Data science is an interdisciplinary field that plays an increasingly important role in a range of occupations. The Master’s programme is therefore open to anyone, which implicitly means that students will have a wide variety of backgrounds in these subjects. The programme is based on the competencies that Bachelor's students of business administration typically acquire at a university of applied sciences or a university in Switzerland. If you are new to these fields, you must be willing to learn the basics during the first semester, and the respective preparatory modules therefore count as part of the programme.

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We will help you with arranging a semester abroad but recommend that you go at the earliest in the third semester. While Lucerne University of Applied Sciences and Arts has an extensive network of partners, the International Office also helps students with preparing for their semester abroad outside of this network. 

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