DS834: Data Science for the Metaverse

Comment

Elective course for the Human Informatics profile – Data science.

Entry requirements

A Bachelor’s degree from the humanities or social science or relevant Bachelor of profession.

Academic preconditions

Students following the course are expected to

be able to apply simple data analysis of a given data material with R.

Course introduction

The Metaverse, is often described as the next iteration of the Internet, a single universal digital environment, of interconnected current and emerging technologies, that provides a wide diversity of real-time immersive experiences for business, education, health, entertainment, social activities, etc.

The course provides with the main theoretical and practical principles of data analytics and information architecture for the design and optimization of Metaverse applications. The student will address issues of data collection from multiple sensors, input devices and user’s behaviours, as well as data exploration, analytics and decision processes, while analysing a Metaverse scenario.

In relation to the competence profile of the degree it is the explicit focus of the course to:

give competence to understand and apply relevant Data Science skills for the design of different digital realities applications.
give competence to understand and apply data analytics for the design and optimization of user experiences, interactions and content consumption in a given scenario.
give competence to identify issues related to data privacy and security and how these should be addressed to ensure the design and development of sustainable and trustable applications.

Expected learning outcome

The learning objective of the course is that the student demonstrates the ability to:

to identify methods, processes and information flow necessary to stablish information architectures for applications in the Metaverse.
to identify relevant data collection technologies used in Metaverse applications, such as sensors, input devices, user’s behaviours, etc.
to apply Data Science skills that supports the data-driven design of Metaverse applications.
to find publicly available data sets collected from relevant data sources in order to evaluate different data analytics approaches.
have knowledge of modern technologies used in the implementation of Data Driven Design of Metaverse applications.

Content

The following main topics are contained in the course:

Current and predicted state of user experiences (e.g., Virtual-, Augmented- or Mixed- Reality [XR]) and other applications in the digital reality known as the Metaverse.
Theoretical and practical principles of data analytics for Metaverse applications.
Data-driven design principles, methods and processes to implement applications in a given Metaverse scenario.
Tools for identification, retrieval analysis and management of publicly available data sets for proof of concepts
Issues related to data privacy and security and how these should be addressed to ensure the design and development of sustainable and trustable applications.

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

January

Tests

Home assignment

EKA

N340118102

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Duration

7 days

Examination aids

To be announced during the course.

ECTS value

Additional information

Scope: max. 10 standard pages excl. front page, table of contents, bibliography and appendices.

Indicative number of lessons

26 hours per semester

Teaching Method

At the faculty of science, teaching is organized after the three-phase model ie. intro, training and study phase.

Intro phase: 8 hours
Training phase: 18 hours, hereof tutorials: 10 hours and laboratory exercises: 8 hours

Activities during the study phase:

Solution of weekly assignments in order to discuss these in the exercise sections.
Solving the project assigments
Self study of various parts of the course material.
Reflection upon the intro and training sections.

Feedback: students receive feedback on assignment solutions.

Teacher responsible

Name	E-mail	Department
Rocio Chongtay	rocio@sdu.dk	Institut for Medier, Design, Læring og Erkendelse

Timetable

Odense

Show full time table

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Profile	Education	Semester	Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study.

If a transitional arrangement has been made for a course, it will be stated in the list.

See transitional arrangements for all courses at the Faculty of Science.