UC3DVS10 Data Visualisation

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UC3DVS10 Data Visualisation

Course description
- NQF Level
  Bachelor's degree (Level 6 1. Cycle)
- Area of Study
  Computing
- Program of Study
  Applied Data Science
- ECTS
  10
- Campus
  Kristiansand, OnlinePLUS - Oslo, Online
- Course Leader
  Rayne Reid

Introduction

Language of Instruction and assessment: English
May be offered on Campus and Online.
May be offered as a separate course.

Included in the following bachelor's degrees:

Applied Data Science

Course Aim(s)

The course aims to provide students with the theoretical and practical knowledge and skills to interpret and present the results of the analysis of large datasets using suitable formats and mediums, in appropriate contexts, and to appropriate levels of granularity. A range of example data sets, from a selection of subject domains, will be used for both illustration and practical activities.

Course Learning Outcomes

Knowledge

The student has knowledge of

K1	historical and emerging research and developments in the field of data visualisation, specifically with regard to big data.
K2	the principles and challenges of creating and evaluating effective data visualisations.
K3	how to select, apply and critically evaluate tools and techniques for generating effective and situation-appropriate data visualisations.

Skills

The student gain skills in

S1	ability to select and apply a selection of appropriate data visualisation tools, technologies and techniques to specific datasets.
S2	ability to critically evaluate the efficacy of data visualisation tools, technologies and techniques.
S3	critically evaluate and compare data visualisation scholarly matter and case studies.
S4	recognise and interpret the strengths and limitations of various visualisation techniques, in order to critically select and apply them to a given problem.

General Competence

The student can demonstrate

G1	identifying and understanding relevant legal and ethical issues relating to visualisation of data.
G2	the challenge of developing and communicating meaningful data visualisations.
G3	critical reflection on the application of data visualisation tools and techniques.

Course Topics

Introduction to Data Visualisation
Aspects of Data Visualisation
Geospacial Data Visualisation
The Art of Data Visualisation

Teaching Methods

Teaching will be based on a hybrid-flexible approach. Instructor-led face-to-face learning is combined with online learning in a flexible course structure that gives students the option of attending sessions in the classroom, participating online, or doing both.
All activities require active student participation in their own learning.
Learning delivery methods and available resources will be selected to ensure constructive alignment with course content, learning outcomes and assessment criteria.
Students will be taught using a mixture of guidance, self-study, and lecture material. Topics will be introduced in a series of weekly lectures. The guidance sessions will be directed practical exercises and reading in which students can explore topics with support from a teacher. This material will also require students to self-manage their time to ensure tasks are completed and the theory is fully understood. This will allow the students to fully engage with lectures and with their peers.

Resources and Equipment

Learning resources are available in the LMS and include, but is not limited to:
- literature and online reading material (essential and recommended)
- streams, recordings and other digital resources, where applicable
- video conferencing and communication platforms, if applicable
- tools, software and libraries, where applicable
Students must have access to an internet connection, and suitable hardware.
- Accessing live streams and virtual laboratories requires a minimum broadband connection of 2Mbps (4Mbps recommended).
Students working on their own laptop/computer are required to acquire appropriate communications software, a webcam, and headphones (or a similar alternative).

Prerequisite Knowledge

UC2SAT10 Statistical Analysis: Tools and Techniques, and UC3MAL10 Machine Learning, or equivalent course(s).

Reading List

The reading list for this course and any additional electronic resources will be provided in the LMS.

Study Workload

250 nominal hours.
Study workload applies to both Campus and Online students.

Activity	Duration
Teacher-led activity	24 Hour(s)
Teacher-supported work	48 Hour(s)
Self-study	178 Hour(s)

Work Requirements

There are no mandatory assignments on this course.

Assessment Strategy

This course has three (3) exams contributing towards the overall and final grade of the course.

All exams must be assessed as passed to receive the final Course Grade.

Form of assessment	Grading scale	Grouping
Portfolio of Work	A-F	Individual
Portfolio of Work	A-F	Individual
Presentation	A-F	Individual