TH Köln

Master Digital Sciences

Documents for Study Program Accreditation

Module »Data Science and Ethics« (DSE)

Organizational Details

Responsible for the module
Prof. Dr. Olaf Mersmann (Faculty F10)
Lecturer(s)
Prof. Dr. Boris Naujoks (Faculty F10), Prof. Dr. Thomas Bartz-Beielstein (Faculty F10), Prof. Dr. Olaf Mersmann (Faculty F10)
Language
English
Offered in
Summer Semester (Duration 1 Semester)
Location
Campus Gummersbach, or remote
Number of participants
minimum 5, maximum 35
Precondition
Basic understanding in data analytics or machine learning
Recommendation
Interest in programming and data literacy.
ECTS
6
Effort
Total effort 180h
Total contact time
120h (30h lecture / 30h exercise / 60h practical)
Time for self-learning
60h (containing 60h self-organized project work)
Exam
Project work in conjunction with portfolio creation and expert talk
Competences taught by the module
Analyze Domains, Model Systems
General criteria covered by the module
Interdisciplinarity, Digitization

Mapping to Focus Areas

Below, you find the module's mapping to the study program's focus areas. This is done as a contribution to all relevant focus areas (in ECTS, and content-wise). This is also relevant for setting the module in relation to other modules, and tells to what extent the module might be part of other study programs.

Focus Area ECTS (prop.) Module Contribution to Focus Area
Acting Responsibly 2

Students are taught how to responsibly run data science projects by taking into account ethics, privacy and safety aspects during all phases of the project.

Architecting and Coding Software 1

Students will design and analyse experiments using modern statistical programming packages. They will implement different optimization strategies in exercises or projects.

Designing Innovations and Products 1

Students will learn to include ethical considerations into the design process for innovative products.

Generating and Accessing Knowledge 2

Students are taught the importance of careful planning when aquiring data, be it in a controlled setting (i.e. DoE) or by reusing existing data. Special emphasis is placed on techniques to avoid or reduce biases.

Learning Outcome

Students will learn a holistic approach to running successfull data science projects by

  • taking ethical, privacy and safety concers under consideration,
  • planning and designing schemes to collect data,
  • avoiding biases during data collection and analysis,
  • Coding / Optimization / Programming
  • and communicating results in clear and precise terms.

With the tools and concepts taught, they will be able to successfully run complex data science projects.

Module Content

  1. Design of Experiments - planning for a better outcome
  2. Optimization - the core of modern machine learning
  3. Ethics - first, do no harm
  4. Data Protection - it’s the law!

Forms of Teaching and Learning

  • Lectures
  • Exercises
  • Data analysis projects
  • Software development

Learning Material Provided by Lecturer

  • Selected literature and web resources
  • Slides and handouts for the lectures
  • Exercises
  • Tutorials, example code, and datasets
  • Learning nugget videos

Literature

  • Montgomery, D. C. (2006). Design and Analysis of Experiments. John Wiley & Sons.
  • Goos, P., & Jones, B. (2011). Optimal Design of Experiments: A Case-Study Approach. John Wiley & Sons.
  • Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
  • Aggarwal, C. (2020). Linear Algebra and Optimization for Machine Learning: A Textbook. Springer.
  • James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
  • MacKay, R. J., & Oldford, R. W. (2000). Scientific Method, Statistical Method and the Speed of Light. Statistical Science, vol. 15, no. 3, pp. 254–278.
  • Spiegelhalter, D. (2020). The Art of Statistics: Learning from Data. Pelican.
  • Wing, J. M. (2019). The Data Life Cycle. Harvard Data Science Review, 1(1). (link)
  • O’Neil, C. (2016). The Ethical Data Scientist. Slate. (link)
  • Peng, R. (2018). Trustworthy Data Analysis. (link)
  • ASA Ethical Guidelines for Statistical Practice (link)
  • ACM Code of Ethics and Professional Conduct (link)
  • Oxford - Munich Code of Conduct (link)
  • BMVI Richtlinie zu autonomen Fahrzeugen (link)
  • Gewissensbits der Gesellschaft für Informatik (in German, link)