Official course description:
Full info last published 15/11-19

Data-Driven Design & Development

Course info
Language:
English
ECTS points:
7.5
Course code:
KGDADDD1KU
Participants max:
75
Offered to guest students:
yes
Offered to exchange students:
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
MSc in Games
Staff
Course manager
Assistant Professor
Teacher
PhD student
Teacher
Part-time Lecturer
Teaching Assistant
Assistant Lecturer
Course semester
Semester
Forår 2020
Start
27 January 2020
End
31 August 2020
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract

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.

Description

Data analysis 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 user data is gathered, and how data analysis can be used in order to support the design and development of games and other interactive systems. They also learn how to develop, use, and analyse different game industry related metrics (e.g. customer metrics, community metrics, performance metrics, gameplay metrics) and how to use those to balance and optimise different aspects of games, informing the design process.

Formal prerequisites
The students need to have attended a introduction to programming course.
Intended learning outcomes

After the course, the student should be able to:

  • Describe the impact of data analysis 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.
  • Relate qualitative properties of the game experience with quantitative measures.
  • Outline and apply relevant statistical measures for testing and evaluation
  • Design tests and experiment and evaluate their results in relation to the game design and development
  • Construct effective visualisations, from collected metrics, that can inform game design, production, and marketing.
  • Describe the challenges connected to the collection and analysis of large amount of in-game data.
Learning activities

Students are responsible for attending weekly lectures, participating in the group activities and engaging withe the teaching staff for supervision and feedback.

Mandatory activities
There is one mandatory activity in which the students will be required to submit a report with a number of statistical analyses and visualisations based on a given dataset containing user behaviour from a game. The purpose of the mandatory activity is to practice the analysis of data on a clean and well designed dataset before starting to work on self-collected data in the course project.
Furthermore, this will also give an opportunity to the students to practice working on a large dataset that would be otherwise impossible to gather on their own.

After the submission, the report will be evaluated by the course teachers and oral feedback will be given during a special lecture. The students that failed in the first attempt will have the opportunity to submit a new version of the report within the final submission deadline for the course.

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

Course literature

The course literature is published in the course page in LearnIT.

Student Activity Budget
Estimated distribution of learning activities for the typical student
  • Preparation for lectures and exercises: 26%
  • Lectures: 13%
  • Exercises: 7%
  • Assignments: 7%
  • Project work, supervision included: 22%
  • Exam with preparation: 25%
Ordinary exam
Exam type:
D: Submission of written work with following oral, external (7-trinsskala)
Exam variation:
D22: Submission of written work with following oral exam supplemented by the work submitted.


reexam
Exam type:

Exam variation: