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 about Data Engineering and Information Retrieval. It covers methods and technologies for managing, processing and analyzing potentially large and distributed data collections for transactional or analytical use, including multi-model databases and NoSQL stores. And it covers also mastering data in unstructured form (full text search). The course consists of four parts: 1. Database Management; 2. Data Warehousing and Data Analytics (Business Intelligence); 3. Data Integration including Data Synthesis; and 4. Information Retrieval.

Prerequisites

  • UML Class Diagrams
  • Relational Models, Relational Algebra
  • Relational Database Management System (RDBMS) Architectures
  • SQL:92 (i.e. queries with SELECT-FROM-WHERE-GROUP BY)
  • Normalization, Query Optimization, Indexes
  • Transaction Processing, Concurrency Control
  • Security in Relational Database Systems

Learning Objectives

This module covers following important aspects of Data Engineering:

  • Students understand the use of modern database technologies for processing and managing potentially large and distributed data collections for transactional or analytical use.
  • Students will be proficient in modern query languages such as the post-relational SQL (SQL:2023 and newer).
  • Reaching beyond RDBMS, students learn about data structures (data types) and know which of these to use depending on the requirements and type of data available (polyglot persistence, multi-model databases).
  • Students know NoSQL stores and selected cloud data stores.
  • Students know methods and tools to integrate, to cleanse and to synthesize data.
  • Students know how to deal with full text information using databases and search engines (information retrieval; prompt engineering).
  • Students can also apply the acquired knowledge in their own working environment.

Contents of Module

The course is divided into four parts:

 

  1. Database Management (DB): New data structures (types) and alternatives to RDBMS. Storing data with post- and non-relational aspects, including NoSQL technologies (especially graph databases), and a selection of advanced topics such as cloud or vector databases.
  2. Data Warehousing and Data Analytics (DW): Methods and tools for data aggregation and data analytics such as the ones involved in business intelligence.
  3. Data Integration (DI): Methods and tools for data integration, data cleansing and data synthesizing (e.g. for training and testing) are explained.
  4. Information Retrieval (IR): Methods and tools for finding information in full text using databases and (enterprise) search engines, including crawling.

 

Weighting between the parts will be confirmed at the beginning of semester. Tentative weighting:  

 

  1. DB: ~4-6 weeks
  2. DW: ~2-4 weeks
  3. DI: ~1-3 weeks
  4. IR: ~3-5 weeks

 

 

Teaching and Learning Methods

Frontal teaching, case studies, exercises, discussions, (group) work assignments (i.e. laboratory work or mini-project).

Literature

Optional literature suggestions (books):

  • DB: Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. R. Wiese. De Gruyter Textbook. 2015. ISBN 978-3-11-044140-6.
  • DB: SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis. R. Teate. Wiley. 2021. ISBN 978-1-119-66936-4.
  • IR: Introduction to Information Retrieval. C.D. Manning, P. Raghavan, H. Schütze. Cambridge UP, 2008.
  • IR: Information Retrieval in Practice. B. Croft, D. Metzler, T. Strohman. Pearson Education, 2009.

Download full module description

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