Data form the basis for many products and services that shape our everyday lives. The MSE in Data Science provides you with the most important methods and tools to process and efficiently organise data, analyse them and use them to generate innovative data-driven products.
Outline of the profession
Professional data scientists are contributing to the design, development and deployment of a huge variety of data products or data pipelines for enterprises and public institutions. The Swiss employment landscape offers you jobs in data engineering, data analysis or data-driven services. From the collection and processing of data, to machine and deep learning or developing data products – you will find the challenges and opportunities to shape your future in nearly every industry.
The MSE in Data Science is based on three thematic pillars:
- Data analytics
- Data engineering
- Data-driven products and services
Graduates of the MSE in Data Science are able to apply statistical methods to describe and explore data as well as to draw conclusions from them. You will build data-driven models by using data mining, machine learning and deep learning. You will organise the collection and sourcing of application-specific data from heterogeneous sources. You will also be capable of planning and organising the storage of such data. Developing applications able to process data streams, extract features and apply models to them will be another of your competencies. You will learn to understand business needs with regard to data pipelines and turn analytical results into actions. In addition, you will understand non-technical constraints related to ethics, privacy, security and safety as they relate to data processing in enterprises and public institutions.
Entry skills and enrolment process
Interested students will be individually assessed for their suitability by the respective University of Applied Sciences. The assessment of the entry skills is part of the enrolment process of the respective school.
Recommended theory modules
The theory modules comprise 30 out of 90 ECTS. The modules are taught by professors from all over Switzerland at various locations. You will take these modules together with other MSE students. You can find the recommended theory modules for the MSE in Data Science (DS) here.
Additional skills relative to Bachelor of Science
The MSE Data Science profile will complete your education in the fields of data engineering, data analytics and data-driven products, including advanced competencies in statistics, data mining, machine learning, deep learning, as well as the design, implementation and deployment of data-driven products for enterprises.
Universities of Applied Sciences (UAS) offering the Data Science profile are shown below. Find out more about the courses of study at the individual UASs.
Specialisation studies in German or French (Biel), theory modules in English, German or French
- Data engineering for big data applications
- Analysis of text and graph data
- Applications for data visualization and analysis in companies
- Smart applications in legal tech and life sciences
In the specific implementation of the MSE Data Science profile at the FHNW, students will acquire sound skills in statistical data analysis, data engineering, machine learning and deep learning. A strong focus is put on effectively applying Data Science methodology in result-oriented, applied research projects with industry and research partners.
Central modules (30 ECTS) and deepening modules (18 ECTS) are given in English and French. Deepening projects (2x6 ECTS) and Master thesis (30 ECTS) can be realized either in English or in French.
The Data Science profile (DS) at HES-SO aims at acquiring deep practical and methodological competences in data analytics, artificial intelligence, deep learning, big data, information visualization and, generally speaking, systems for data services that are interoperable, reliable and scalable.
German and English
- Application of statistical methods, Data Mining, Machine Learning and Deep Learning.
- Focus on Data Engineering, Data Analytics and Data Services.
- Design, development and deployment of data products and data pipelines in companies and public institutions.
Mix of English and German
- Application of data science methods with a focus on industrial problems.
- Interdisciplinary data science projects at the interface of computer science, geospacial statistics, logistics, business intelligence, etc.
- Usage of powerful computing hardware.
- Bayesian methods for probabilistic machine learning (Bayesian networks, Bayesian optimization, etc).
- Deep Learning for Computer Vision.
- Natural language processing.
- Projects with national and international companies.
English only or mix of English and German
- Database and Information Systems, Big Data Analytics, Information Retrieval
- Artificial Intelligence, Computer Vision, Machine and Deep Learning, Signal Processing, Statistical Data Analysis, Text Analytics
- Developing Data-Driven Services