Management of digital enterprises and digital leadership
The increasing datafication has enormous potential for product and service innovations, disruptive business models and entirely new ways of designing and managing business processes. Students will therefore study business models that become feasible only by using cutting-edge data technologies and analytical methods, which means analysing the challenges and opportunities that come with datafication as seen from the perspective of organisations. They will also address the respective legal framework and tackle the ethical issues that arise from the use of data in a wide-ranging context.
Data-driven innovation and the data innovation ecosystem
Students will familiarise themselves with innovation techniques and explore the ecosystems that spawn innovative forms of data use. They will also manage class projects on organising collaborative innovation events (e.g. hackathons) together with partners from the applied fields.
Designing data products and services
Digitalisation and datafication influence our social behaviour and change the way we interact and communicate with each other. This topic therefore aims to make students more aware of the social dimension of data technologies by teaching them how to design data products and services consistently as seen from the perspective of the potential users. Furthermore, students learn how visualisation and narrative techniques can help to process the results of data analyses so as to have the desired impact on the target group.
Fundamental data engineering for data scientists
Students receive an in-depth introduction to computer science concepts that are important for data scientists. They are able to access, merge, structure and prepare data from different sources for analyses by using the Python programming language as their instrument.
Data analytics and research methods for data scientists
Students receive an in-depth introduction to statistical concepts that are important for data scientists by learning how to design and carry out statistically valid data experiments. They will also learn the basics of data modelling, practice using predictive analytical procedures (including applied machine learning) and thoroughly understand how to analyse special data types (trend, panel, event data, etc.). For this they will use the statistics programme R as their instrument.
Scale up! Working with really big data
Students tackle specific problems concerning the storage, processing and analysis of very large data quantities. They will learn how to work with big data and how to tailor the techniques and methods as needed in order to arrive at meaningful insights.
Advanced analytics for unstructured data
The largest data collections comprise digital information without any formal structure, as in the case of digital texts and recordings of the human voice. During this group of modules, students become familiar with advanced methods for analysing text, image and audio data.
Managing data projects and personal communication
Students learn how to design data science projects so as to effectively support decision-making in a wide range of contexts. This means setting up and successfully managing challenging projects that require not only strong competencies with respect to content and design but also excellent communication skills.
Domaine experience – real-life data science in action
An essential part of the course involves working on projects with different data types (transaction, process, health, financial, media, mobility data, etc.). Students therefore gain deep insights into applied data science in a wide range of contexts and learn to apply their theoretical knowledge to concrete issues relating to the applied fields, which serve as basis of their project work whenever possible.
Master thesis (incl. preliminary study)
The Master’s thesis is the culmination of the Master’s degree programme whereby students manage real-life data projects with their partners in Switzerland and abroad. Students with a strong interest in academic work have the opportunity to participate in or launch a research project of their own. The Master’s thesis can also serve to develop an innovative data product or data-driven business model as the basis for a start-up.