Value derived from data is the core of the Master’s programme
Management of digital enterprises and digital leadership
The growing datafication trend harbours enormous potential for product and service innovations, disruptive business models and entirely new ways of designing and managing operations. During the programme, students will therefore analyse business models that become possible only when applying cutting-edge data technologies and analytical methods, and they will learn about the challenges and opportunities relating to data cleansing as seen from an organisation’s perspective. In addition, they will study the legal framework and ethical issues associated with large-scale data use.
Data-driven innovation and the data innovation ecosystem
The programme explores the innovation techniques and ecosystems that lead to innovative forms of data use. Students therefore work on class projects in which they organise innovation events (e.g. hackathons) together with partners from business and elsewhere.
Designing data products and services
The Master's programme also examines the human-centred and social dimensions of data technologies, and students therefore learn to design data products, processes and services as seen from the perspective of potential users. In addition, they will learn about preparing their findings and communicating them effectively by using suitable visualisation and narrative techniques.
Fundamental data engineering for data scientists
Students receive a thorough introduction to computer science concepts that are important for data scientists. They will learn how to access, integrate, organise and prepare data from a wide range of sources for analytical purposes and to use the Python programming language as their working instrument.
Data analytics and research methods for data scientists
Students learn to design and carry out statistically valid data experiments. This includes understanding the basics of data modelling, applying the latest methods of predictive analytics (including applied machine learning) and learning how to analyse special data types (trend, panel, event data, etc.) with the statistics software R.
Scale up! Working with really big data
The Master's programme addresses specific issues relating to storing, processing and analysing very large data volumes. Students learn how to work with big data and to make the technical and methodical adjustments necessary for extracting the information it contains.
Advanced analytics for unstructured data
The largest data sets comprise digital information in unstructured form, such as digital texts and voice recordings. During these modules, students learn about the advanced methods used for analysing text, image and audio data.
Managing data projects and personal communication
Students learn to design data science projects as means of effectively supporting decision making in a broad range of contexts. To this end, they will learn how to set up, communicate and successfully complete challenging data projects.
Domaine experience – real-life data science in action
An essential part of the programme involves projects with data types as found in marketing, finance, production, logistics, mobility, media, life science, open government, etc. Students therefore learn to examine in depth the possibilities of data science in a wide range of contexts and discover ways of applying their theoretical knowledge to specific problems encountered in real life.
Master’s Thesis (incl. preliminary study)
The Master's programme culminates in the Master's Thesis during which students work on real-life data projects with partners in Switzerland and abroad. Those with a strong interest in doing research will have the opportunity to join a research team or to launch a project of their own. They can also use the Master’s Thesis to develop an innovative data product or a data-driven business model for a start-up.
