How You Study With Us

In the Bachelor's program in Data Science, we say: goodbye to lectures, welcome to flexible, networked, and practice-oriented learning!

Learning to Learn

The learning concept of the Data Science program focuses on the ability for lifelong learning. This means you'll be empowered to learn quickly and according to your needs – even after your studies. This is particularly important in a field like Data Science, where new developments occur almost daily.
To engage in lifelong learning and succeed in a changing work environment, you should, among other things...

learn how to study independently
ask relevant questions and strategically develop your own knowledge
build new skills that make your work non-automatable

Practice-Oriented

Knowledge alone is not enough; you must also be able to apply it. In our study program, you can therefore work on one or two challenges or projects per semester – up to a third of your studies – addressing applied questions in a team. What you learn in the modules is thus connected and applied in a realistic way, just as it will be in your professional life. In challenges and projects, you work on:

questions from industry partners
questions from faculty, tailored to be realistic and appropriate for your level of study
your own questions, from your job, hobby, or passion

Examples of Projects and Challenges

Climate Data Story
Students develop a new website that clearly and comprehensibly presents data on the development of extreme climate events (heavy rainfall, droughts, storms, forest fires, floods, etc.) in your region.
Understanding Logistics Costs
Since the outsourcing decision, logistics costs in the company have been continuously increasing. The client wants to create transparency about the cost drivers in order to take appropriate cross-selling measures.
Cross-selling in Banking
Students analyze customer and transaction data from a bank with the aim of identifying additional suitable products for existing customers.
Rockfall Risk
Along an Alpine road, repeated minor rockfall events have occurred, and authorities are wondering if the risk for road users is acceptable. Students provide the answer using data-based risk calculation.
Weather Monitor for Sailors
Students create a weather monitor on a Raspberry Pi where sailors can read weather data and forecasts for the coming hours.

How Much is My House Worth?
Students create a machine learning model that can estimate the value of a property based on a few key features.
Tinder for Movies
We know it from Netflix and YouTube: an algorithm suggests movies based on our preferences. In Tinder for Movies, students implement exactly that.
Video-Based Tennis Training
Data on ball and player movement is extracted from videos of tennis training and matches, statistically evaluated, and visualized to improve and individualize the training of young players.
Shared Mobility
In collaboration with a research group, students analyze the demand for shared mobility services in Swiss cities with the aim of aligning the supply temporally and spatially to the demand.
Café, mon amour!
For the coffee machine manufacturer Thermoplan, students analyze coffee preferences in different countries to best customize their products. In another project for the same client, students create a predictive maintenance algorithm to optimize the timing of coffee machine maintenance.

Deep Learning in the Wild
Students develop a deep learning image recognition algorithm that recognizes and categorizes wildlife in photos.
MRI to CT
In collaboration with the University Hospital Zurich, students develop a diffusion model that allows precise translation of an MRI image into a CT image.
Mobility Sensor
Data Students create a deep learning model that recognizes which activity they are currently performing based on the movement data of their mobile phone.
Recognizing Bone Fractures
Students develop a deep learning model for a hospital in Liestal that detects bone fractures on X-rays.

Flexible

Flexible learning is emphasized in the Data Science program:

There are no fixed semester plans that everyone needs to adhere to, but a flexible module selection (the only mandatory module is the Bachelor's thesis); most modules are offered every semester. You can individually design your content focus.
You choose your learning time, place, and pace yourself. Thanks to a digital learning platform with learning materials, information, and forums, you can learn flexibly.
There are no regular lectures, but workshops and optional contact hours where you can interact weekly with faculty and coaches.
In general, you have few mandatory appointments. Our students come to campus two days per week on average: to meet up with fellow students and faculty, for contact hours, and for workshops.
For a large part, you will collaborate with other students to complete assignments, to regularly learn with and from each other. For that matter, suitable learning spaces and open co-working zones, as well as a digital learning platform, are available to you.

Portrait of Julia Lobaton, Student BSc Data Science — Julia Lobaton, Student BSc Data Science

Individual Coaching

What you would otherwise have to acquire independently and laboriously at the beginning of your career is part of our program: Our experienced coaches personally support you in developing self-competence, social competence, leadership, and career planning. Has everything turned topsy-turvy again? Our coaches are also happy to support you with challenges.

Our Coaching Approach

The collaboration between students and coaches is based on appreciation, reliability, and transparency. The content discussed is treated confidentially by both sides. Collaboration with coaches takes place primarily in the form of individual coaching. Independent exploration of selected topics and exchanges with other students (peers) complement the coaching.

kreislauf der Begleitung durch das coaching

Interdisciplinary

As a Data Scientist, you always work with specialists from other disciplines, which requires the right skills. Our learning concept enables the acquisition of future skills: Whether communication and collaboration, creativity, or critical thinking – in individual modules as well as in large team projects, you'll find various opportunities to further develop these important competencies
In challenges and projects, you learn to think about and understand other fields and domains, what relevant aspects are, and how you can create the greatest benefit from the data.

Portrait of Gabriel Torres Gamez, Student BSc Data Science — Gabriel Torres Gamez, Student BSc Data Science

International

Spain, Poland, Sweden, France... Exchange students from all over Europe enrich our daily study life. The Data Science program is very popular among exchange students due to the module offerings, study format, and social exchange.

As a Data Science student, you naturally also have the opportunity to study for one or two semesters at one of our partner universities. You can get more information from the International Office.

Weltkarte mit Fahnen, die potentielle Auslandssemester-Ziele markieren