Data Visualisation

H3ABioNet_high_res

Prepared by: Raphael D. Isokpehi
Module name: Data Visualisation
Contact hours (to be used as a guide): Total (40 hrs), Theory (60%), Practical (40%)

SPECIFIC OUTCOMES ADDRESSED

On completion of this module, students should be able to:

1. Describe the challenges associated with digital data.
2. Distinguish between visualization tools (VTs), visual representations (VRs), and the process of visual encoding.
3. Describe how visual representations affect the performance of high-level cognitive activities. 4. Identify theoretical frameworks and design principles for human-information interaction
5. Align categories of expertise (instruction, judgement, prediction and performance) to data visualization tasks.

BACKGROUND KNOWLEDGE REQUIRED

Pre-requisite H3ABioNet bioinformatics modules: No pre-requisite
Additional: Not applicable

BOOKS AND OTHER SOURCES USED

1. Parsons, P., & Sedig, K. (2014). Common visualizations: Their cognitive utility. In Handbook of Human Centric Visualization (pp. 671-691). Springer New York. http://web.ics.purdue.edu/~parsonsp/papers/HCV-CV2013.pdf
2. Carter, M. G., Hipwell, P., & Quinnell, L. (2012). A picture is worth a thousand words: An approach to learning about visuals. Australian Journal of Middle Schooling, 12(2), 5-15. http://eprints.qut.edu.au/55420/1/55420A.pdf
3. Sedig, K., Parsons, P., Dittmer, M., & Ola, O. (2012). Beyond information access: Support for complex cognitive activities in public health informatics tools. Online journal of public health informatics, 4(3). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615827/
4. Sedig, K., & Parsons, P. (2013). Interaction design for complex cognitive activities with visual representations: A pattern-based approach. AIS Transactions on Human-Computer Interaction, 5(2), 84-133. http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1057&context=thci
5. Isokpehi, Raphael D, Wootson, Kiara M, Smith-McInnis Dominique R, and Simmons, Shaneka S. Interactive Analytics for Complex Cognitive Activities on Information from Annotations of Prokaryotic Genomes, Journal of Computational Science Education, v.8, 2017, p. 29-36. http://www.jocse.org/articles/8/2/5/
6. Sacha, D., Stoffel, A., Stoffel, F., Kwon, B. C., Ellis, G., & Keim, D. A. (2014). Knowledge generation model for visual analytics. IEEE transactions on visualization and computer graphics, 20(12), 1604-1613. https://www.bckwon.com/pdf/kgva.pdf
7. https://www.tableau.com/learn/whitepapers
8. Good Enough to Great: A Quick Guide for Better Data Visualizations https://www.tableau.com/learn/whitepapers/good-enough-great-quick-guide-better-data-visualizations
9. Introduction to Visual Design. http://oli.cmu.edu/courses/free-open/introduction-to-visual-design/

COURSE CONTENT

A) Theory lectures (24 contact hours)

1. Introduction to Data Challenges (3 hours)

• Data Flow (Collection, Storage, Access and Movement)
• Data Curation (Preservation, Publication, Security, Description and Cleaning)
• Data Analytics (Modeling and Simulation, Statistical Analysis and Visual Analytics)
• Complex Information

2. Visualization Tools, Visualizations and Visual Representations (3 hours)

• Classification of Visuals
• Visualization Tools
• Visual Representations

3. Perception and Cognition (3 hours)

• Perception and Visualization
• Cognition and Visualization
• Cognitive Activities and Complex Cognition
• The Relationship Between Direct and Data-Mediated Knowledge of the World
• Complex Cognitive Activities Support Tools

4. Interaction and Interactivity of Visualization Tool (3 hours)

• Levels of Interaction and Interaction Techniques
• Action Patterns
• Interactivity (Quality of Interaction)
• Human-Information Interaction with Complex Information for Decision Making

5. Common Visualizations (Techniques) and their Cognitive Utilities (3 hours)

• Visual Encodings and Marks
• Glyphs and Multidimensional Icons
• Plots and Charts
• Maps
• Graphs, Trees and Networks
• Enclosure Diagrams

6. Design Principles for Visual Representations for Performance of Complex Cognitive Activities (3 hours)

• Appearance, Complexity, Configuration, Density, Dynamism, Fidelity, Fragmentation, Interiority, Scope and Type
• Knowledge generation model for visual analytics

B) Practical Component (Expertise Development Project) (16 contact hours)

Students will develop categories of expertise (instruction, judgement, prediction and performance) in data visualization through practical component.

1. Construction of Datasets
2. Development of Interactive Visual Representations

ASSESSMENT ACTIVITIES AND THEIR WEIGHTS

TESTS (30% weight)
PRESENTATION (20% weight)
PROJECT (30% weight)
HOMEWORK (20% weight)

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