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n.BA.AD.DaSo.23HS (Data and Society) 
Module: Data and Society
This information was generated on: 20 March 2025
No.
n.BA.AD.DaSo.23HS
Title
Data and Society
Credits
2

Description

Version: 1.0 start 01 August 2023

 

Study Programme Applied Digital Life Sciences
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Applied Digital Life Sciences degree programme
Module Type  
X Compulsory Module    Elective Module    Optional Module
Planned Semester 4th Semester
Module Coordinator Victor Garcia
Telephone / E-Mail  +41 (0)58 934 55 46 / victor.garcia@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)		 Victor Garcia
Entrance Requirements Statistik und Wahrscheinlichkeit, Mathematische Modelle und Analyse, Daten und Information, Englisch
Learning Outcomes and Competencies Technical skills:
 
The students are
  • familiar with the modern notion of an algorithm.
  • familiar with the characteristics of algorithms that describe, for instance, an algorithm’s efficiency at solving a particular computational problem.
  • able to reflect, depending on the context, on which algorithm is appropriate for optimizing a particular problem given the variable to be optimized.
  • familiar with several measures of predictive power in the context of machine learning.
  • capable to reflect on the advantages and disadvantages of algorithms depending on the context of use.
  • aware of ethical problems in the application of algorithms and can critically reflect upon the claims made by algorithm providers as to their objectivity for delivering a service.
Transferable skills:
The students will be able to
  • classify, reflect on, and discuss socially relevant events and processes in light of the concepts introduced in the module.
  • critically reflect on, verbalize and discuss cross-disciplinary trends and their impact on specific areas of society, such as for instance the media system.
Module Content
  • Turing Machines and Algorithms
  • Machine learning from the perspective of knowledge extraction from data (Learner); what does it mean to learn from an algorithm’s perspective?
  • Some classically optimal algorithms and their application in everyday decision making: optimal stopping, explore/exploit, sorting.
  • What learners are there and what are their advantages and disadvantages?
  • Risks and opportunities of big data
  • Ethical and social problems. Data ethics and privacy.
  • Impact of data gathering and processing technologies and individuals and societies
Follow-up Modules Economy and Entrepreneurship, Ethics and Law, Modelling of Complex Systems, Neural Networks
Methods of Instruction  Power Point, short tasks, quizzes, group works, teaching videos, practice tasks and exercises, tests, Flipped-Classroom.
Digital Resources We will use the following resources:
  • Reader
  • Teaching videos
  • (Multiple Choice-) Tests
  • exercises (and solutions)
Lesson Structure / Workload  
 Contact Hours 28
 Guided Self-Study 14
 Independent Self-Study 18
 Total Workload 60
Classroom Attendance Attendance is strongly encouraged but not enforced with attendance list
Assessment
  • Mid-semester test 30%
  • Written exam at the end of the semester 70% / e-assessment

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  English
Comments -

 

Course: Data and Society
No.
n.BA.AD.DaSo.23HS.V
Title
Data and Society

Note

  • No module description is available in the system for the cut-off date of 20 March 2025.