Preliminary public information for, subject to change:
Preliminary info last published 18/03-19

Data-Driven Design & Development

Course info
ECTS points:
Course code:
Offered to guest students:
Offered as a single subject:
Price (single subject):
10625 DKK (incl. vat)
MSc. Master
Master of Science in Information Technology (Games)
Course semester
Forår 2020
27 January 2020
31 August 2020

In this course, students learn how to work in data-driven design and development processes and what changes data analysis brings to the design and maintenance of games.


Data science has become one of the driving forces of game design and development. This course teaches data analysis methods, and how they affect design and development.

Students learn how to gather user data, and how data analysis can be used in order to support the design and development of games and other interactive systems.

In this course, students learn about game architectures for data-driven games, design implications and how to employ data-driven techniques in various areas of game development.

They also learn how to develop, use, and analyse different game analytics (customer metrics, community metrics, performance metrics, gameplay metrics) and using those to balance and optimize different aspects of games, informing the design process.

Intended learning outcomes

After the course, the student should be able to:

  • Describe the impact of data analytics on game design, production, and marketing.
  • Identify the stages of game design, production, and marketing in which analytics support decision making.
  • Classify the key performance indicators relevant in each of the design, production and post production stages.
  • Outline testing and evaluation procedures and relevant statistical measures.
  • Relate qualitative properties of the game experience with quantitative measures.
  • Evaluate analytics solutions in relation to development in business needs.
  • Describe the challenges connected to the collection and analysis of large amount of in-game data.
  • Construct effective visualisations, from collected metrics, that can inform game design, production, and marketing.
Ordinary exam
Exam type:
D: Written report with oral defence, external (7-trinsskala)
Exam variation:
D22: Submission of written work with following oral exam supplemented by the work submitted. The oral exam will be supplemented by the submitted work, i.e. the submitted work supplements a fixed syllabus from the course base.