MSE Master of Science in Engineering

The Swiss engineering master's degree

Each module contains 3 ECTS. You choose a total of 10 modules/30 ECTS in the following module categories: 

  • 12-15 ECTS in technical scientific modules (TSM)
    TSM modules teach profile-specific specialist skills and supplement the decentralised specialisation modules.
  • 9-12 ECTS in fundamental theoretical principles modules (FTP)
    FTP modules deal with theoretical fundamentals such as higher mathematics, physics, information theory, chemistry, etc. They will teach more detailed, abstract scientific knowledge and help you to bridge the gap between abstraction and application that is so important for innovation.
  • 6-9 ECTS in context modules (CM)
    CM modules will impart additional skills in areas such as technology management, business administration, communication, project management, patent law, contract law, etc.

In the module description (download pdf) you find the entire language information per module divided into the following categories:

  • instruction
  • documentation
  • examination 
Data Management (TSM_DataMgmt)

This course is centered on the Data Engineering domain.

This course covers modern methods and technologies that are needed to manage and process potentially large, heterogeneous and distributed data collections. It  includes diverse technologies frequently used in industrial contexts such as data warehouses,  multi-model databases and NoSQL stores. A focus of the class is also given on Information Retrieval including techniques to efficiently retrieve data that are typically in unstructured form. 


  • Basic data structures and algorithms
  • Working level on basic relational databases
  • Relational Models, Relational Algebra,
  • Normalization
  • RDBMS architectures
  • Transactions
  • SQL:92
  • Query optimization, Indexes
  • Security in RDBMS

Learning Objectives

Learning objectives and acquired competencies:

The learning objectives are directed towards Data Engineering:

  • Students understand the modern ecosystems currently used in industries for data storage and data processing; including their respective adequations to application needs.
  • Students understand the use of modern database and processing technologies for managing large, distributed and potentially heterogeneous data collections.
  • Students are able to organize complex data structures, reaching beyond RDBMS and meeting the requirements of data availability and type, e.g. polyglot persistence and multi-model databases.
  • Students are able to use selected advanced data technology stacks such as data warehouses, NoSQL stores and cloud data stores.
  • Students are able to implement methods and tools to integrate, cleanse and synthesize data, such as the ones used to compose data pipelines.
  • Students are able to integrate efficient Information Retrieval techniques typically used for unstructured and textual data, such as the ones used for search engines.
  • Students can also apply the acquired knowledge in their own working environment.


Contents of Module

The module covers the following contents:

1. Database Management (DM): overview of modern ecosystems currently used in industries for data management; new data structures and alternatives to RDBMS; non-relational aspects including NoSQL and cloud data stores; new ways to query data such as JSON paths, SQL extensions, graph query language, etc.

2. Data Integration (DI): Data Warehousing for data aggregation and data preparation for analytics (e.g. business intelligence components); other methods and tools for data integration, data cleansing and data synthesizing.

3. Information Retrieval (IR): Efficient methods for finding information, typically in the context of unstructured and textual data, such as the ones used for search engines; ways to query data in IR systems, such as Query DSL.

Teaching and Learning Methods

Head-on teaching, exercises, case studies.


Optional literature suggestion (books):

  • DB: Lena Wiese: Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. De Gruyter Textbook. 2015. ISBN 978-3-11-044140-6.
  • IR: Introduction to Information Retrieval. C.D. Manning, P. Raghavan, H. Schütze. Cambridge UP, 2008. Classical and web information retrieval systems: algorithms, mathematical foundations and practical issues.
  • IR: Information Retrieval in Practice. B. Croft, D. Metzler, T. Strohman. Pearson Education, 2009.

Download full module description