DATA VISUALISATION

SHE Level 3
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code M3I326700
Module Leader Dawn Carmichael
School School of Computing, Engineering and Built Environment
Subject Computing
Trimester
  • B (January start)

Summary of Content

This module will focus on the fundamental techniques utilised in the field of Data Visualisation. The goad of Data Visualisation is expose the underlying structure of a dataset using visual representations which are targeted towards the human visual perceptual system. In this module, students will learn about various techniques and algorithms utilised in presenting data of various modalities and sources in a format from which information can be easily extracted. Students will build the skills required to produce their own Data Visualisations to allow people to understand the data more quickly or effectively. The essential theories used to develop effective visual representations of data, including case studies of good and bad visualisation examples, will be explored. The Student will learn how to create effective visual representations of data to translate complex data into clear information, allowing the target audience to understand it. They will also learn how to communicate the information clearly using storytelling techniques. After this module, students will be able to appropriately analyse and represent data visually in an appropriate manner using different Data Visualisation tools and Storytelling techniques.

Syllabus

-History of data visualisation -Fundamentals of data visualisation: data, information, visual representation, storytelling -Understanding differences between data visualisation, infographics, scientific visualisation and information visualisation -Effective data visualisation: good/bad data visualisation design -Types of data visualisations: types of graphs, data, audience, visual representation, etc, -Storytelling in data visualisations -Interactive visualisation -Industry Standard tools and technologies -Planning a visualisation: the process of data visualisation design -Building effective data visualisations

Learning Outcomes

On completion of this module, student should be able to:1. Discuss the main methods and tools for data visualization, including the underlying fundamental concepts 2. Select and evaluate appropriate data visualisation techniques to solve real world problems3. Demonstrate how to implement data analysis methodologies for specific problems using appropriate software tools4. Develop data stories that effectively communicate data insights and facilitate decision-making

Teaching / Learning Strategy

Lectures are supplemented by directed reading to relevant sources both hard and electronic format and varied further reading is encouraged. Hands on experience is gained in the process of planning simulated projects. Students are supported in their studies by both face-to-face and on-line tutorials and online quiz material. Student assessment will be based around on-line techniques for three of the assessments and a class-based test for the remaining assessment. Learning and teaching strategies will be developed and implemented, appropriate to students' needs, to enable all students to participate fully in the module.

Indicative Reading

Wilke, Claus O., 2019. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media Kirk, A., 2016. Data visualisation: a handbook for data driven design. Sage. Knaflic, C.N., 2015. Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons. Ward, M.O, Grinstein, G. and Keim, D., 2015. Interactive data visualization: foundations, techniques, and applications. AK Peters/CRC Press

Transferrable Skills

Specialist knowledge and application (A1) Critical thinking and problem solving (D1) Communication skills, written, oral and listening (D14) Effective information retrieval and research skills (D10) Interpersonal skills, team working and leadership

Module Structure

Activity Total Hours
Lectures (FT) 12.00
Independent Learning (FT) 124.00
Assessment (FT) 28.00
Tutorials (FT) 12.00
Practicals (FT) 24.00

Assessment Methods

Component Duration Weighting Threshold Description
Course Work 01 n/a 70.00 35% Practical Project
Course Work 02 n/a 30.00 35% Presentation