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  8. CMPU 4032 Data Visualisation

Computer Science

Grangegorman

Computer Science

Grangegorman

Module Overview

Data Visualisation

Part-time / Level 9 / Online / 5 ECTS

Visualisation facilitates the transformation of data into knowledge. With ever-increasing quantities of data we require assistive methods to help us make sense of, and create value from, the raw information at our disposal. Data Visualisation is a multidisciplinary area drawing upon several different areas of computer science (e.g. psychology, statistics, data mining, graphic design, information visualisation) to deliver meaningful solutions. This module provides students with a brief introduction to the theories underpinning data visualisation, best practice in using visualisations effectively, and practical skills in creating visualisations from datasets. The emphasis of the module is human-centred rather than machine-cantered as a central challenge in visualisation is choosing/designing the best visual interface for a task (as dictated by the expected audience). As a foundational step, learning theories, cognitive science and epistemology will be briefly reviewed: how humans perceive the world; how we make sense of what we perceive; how we absorb information; how to interpret meanings in visualisations; and how we learn and memorise what we have perceived. Lastly, this module will provide a practical introduction to the tools and techniques of data visualisation. Through practical instruction, labs and tutorials, students will be equipped to successfully implement some data visualisation techniques.

 

 

  1. Overview/Fundamentals:

Why Visualization and Its Value: The Purposes for Visualization: Evaluation, Exploration, Presentation

  1. Visual Analysis, Collaboration & History

  2. Perception/Memory: graphical perception, communication

  3. Data: Characteristics & dimensions, data and image models, exploratory data analysis

  4. Design Studies / Visualization Design

  5. Colour

  6. Multidimensional Data Visualization (volume, vector, high-dimensional data, tree and graph)

  7. Mapping, Cartography, Geo-spatial Visualization & Oceanography

  8. Using Space Effectively

  9. Graph Layout and Network Analysis

  10. Text Visualization

  11. Identifying Design Principles

  12. Interaction/Multiples

  13. 3D data visualization and 3D interactive interfaces

  14. Tools for Visualization

The learning methods used to achieve the module learning outcomes will involve a combination of lectures, discussions, case studies, problem- solving exercises, work-based learning, readings, project work, self-directed learning, computer-based learning, video and film.

Formal lectures will be balanced with labs and student participation. Students will be introduced to computer-based data visualisation tools and techniques and then expected to show initiative in acquiring skills necessary. Students will be expected to put theory into action by completing tutorials, exercises and independent experiments using a variety of data visualisation tools.

Lab assignments will focus on providing practice using real-world datasets and real world problems.

Module Content & Assessment

Assessment Breakdown %
Formal Examination 50.00%
Other Assessment (s) 50.00%

Contact school.cs@tudublin.ie for further information.

EU students: €230

Non-EU students: Contact international.city@tudublin.ie for more details.