Official course description, subject to change:
Preliminary info last published 15/11-19

Data Science in Games

Course info
Language:
English
ECTS points:
7.5
Course code:
KGDASCG1KU
Participants max:
50
Offered to guest students:
yes
Offered as a single subject:
yes
Price (single subject):
10625 DKK (incl. vat)
Programme
Level:
MSc. Master
Programme:
Master of Science in Information Technology (Games)
Staff
Course semester
Semester
Forår 2021
Start
25 January 2021
End
28 May 2021
Exam
Exam type
ordinær
Internal/External
ekstern censur
Grade Scale
7-trinsskala
Exam Language
GB
Abstract
This course gives an introduction to the field of data science with applications to game development. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining packages and individual implementation of algorithms.
Description
The course will cover the following main topics:
  • The data mining process 
  • Cluster analysis
  • Data pre-processing 
  • Pattern and association mining
  • Classification and prediction

Application examples will be given from domains including demographics, image processing and games.

Formal prerequisites

Students must have experience with and be comfortable with programming, and be capable of independently implementing algorithms from descriptions. This corresponds to at least having passed an introductory programming course, and preferably also an intermediate-level programming course. The course will contain compulsory programming.

Intended learning outcomes

After the course, the student should be able to:

  • Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
  • Implement basic pre-processing, association mining, classification and clustering algorithms.
  • Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
  • Work efficiently in groups and evaluate the algorithms on real-world problems.
  • Describe different technologies for big data storage and processing
Ordinary exam
Exam type:
D: Submission of written work with following oral, external (7-trinsskala)
Exam variation:
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted.