n.BA.FM.DaInf1.19HS (Data and Information 1) 
Module: Data and Information 1
This information was generated on: 24 July 2024
Data and Information 1


Version: 4.0 start 01 August 2023
Study Programme Facility Management
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Facility Management degree programme
X Compulsory Module
  Elective Module
  Optional Module
Planned Semester 1. Semester
Module Coordinator Institut für Angewandte Simulation / Daniel Bajka
Telephone / E-Mail +41 (0)58 934 50 78 / daniel.bajka@zhaw.ch
Thomas Marcandella
Entrance Requirements -
Learning Outcomes and Competencies Digital technologies are non-optional in today's economy, because digital data provides a competitive advantage for companies and enterprises. Therefore, data science is an important methodological tool for engineers and managers for extracting the relevant information for their analyses, decisions, and steering processes. 

This module focuses on the core concepts of mathematics and computational thinking. Together these concepts provide the foundation for formalising relations, validate them in practice with data, and utilize data to control complex processes. 

The students will be able to ...
  • use mathematical data analytics concepts to solve practical problems and questions
  • apply the core concepts of «computational thinking» in Excel and R
  • organise and structure data
  • logically connect values, value ranges and sets
  • identify and use different data sources
  • identify and describe mathematical relations of distributions and developments
  • visualise data and conduct visual data analysis
  • control simple systems and processes using sensor data.
Module Content
  • Mathematical and computational foundations of the data sciences
  • Data visualisation
  • Open Data and data services
  • Data wrangling using Excel and R
Follow-up Modules Daten und Informationen 2
Methods of Instruction
  • Blended learning using the inverted classroom method
  • Data workshops: application of tools for problem-based assignments
  • Lectures with assignments 
  • Assignment for self-organised individual or group work
Digital Resources
  • Moodle course with interactive assignments and videos
  • Prep-course Mathematics 
  • Prep-course EXCEL 
  • Mathi-Fitnessstudio
Classroom Attendance Requirement -
Assessment Written Exam at the end of the semester (e-assessment): 60%
Graded written exercises (electronic): 40%

If there is a low number of participants, the lecturer may change the form of a repeat examination after consultation with the head of the study programme: e.g. an oral examination can be used to replace a written one. Please report any changes to the form of examinations by e-mail to pruefungsadmin.lsfm@zhaw.ch and Cc. Head of study programme.
Language of Instruction German
Compulsory Reading -
Recommended Reading
  • J. W. Forman (2013) Data Smart: Using Data Science to transform information to insight (Kapitel 1). Indianapolis, IN: Wiley.
  • J. Ross, M. Freeman (2018). Programming skills for data science, start writing code to wrangle, analyze, and visualize data with R. Boston et al.: Addison-Wesley.
  • D. Wollschläger (2017). Grundlagen der Datenanalyse mit R, eine anwendungsorientierte Einführung. 4. Aufl. Berlin, DE: Springer Spektrum.
  • P. Curzon und P. W. McOwan (2018). Computational Thinking (Kapitel 13). Berlin, DE: Springer.
  • H. Wickham (2016). ggplot2, Use R! Cham, CH: Springer Nature.
  • T. Rahlf (2017). Data visualisation with R, 100 Examples. Cham, CH: Springer Nature.
  • J. Albert and M. Rizzo (2012) R by Example, Use R! New York et al.: Springer.
  • B. C. Boehmke (2016) Data Wrangling with R, Use R! Cham, CH: Springer Nature.
  • D. Nolan and D. Temple Lang (2014). XML and Web Technologies for Data Sciences with R, Use R! Cham, CH: Springer Nature.
Comments This module assumes EXCEL (2010 or newer) application knowledge at the level of ECDL AM4. A related prep-course will be available online.


Course: Data and Information 1
Data and Information 1


  • No module description is available in the system for the cut-off date of 24 July 2024.