| CODE | SOC2121 | ||||||||
| TITLE | Principles of Data Visualisation | ||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 5 | ||||||||
| ECTS CREDITS | 4 | ||||||||
| DEPARTMENT | Sociology | ||||||||
| DESCRIPTION | The study-unit aims at exposing students to concepts and techniques for developing appropriate skills to visually represent data in a meaningful and effective way. The analysis of real-world examples will enable students to frame research questions in visual terms, and to address specific goal-oriented and theory-driven research questions by selecting the most appropriate visual analytical strategy. Hands-on use of free software will promote learning by doing. By the end of the study-unit, student will be able to locate, understand, and visually portray patterns in raw data in ways that are meaningful for the tasks at hand. Students will also be able to critically consider the type of data, the intended goals, and the target audience, and to critically evaluate the soundness and shortcomings of any data visualisation design. Study-Unit Aims: - To provide an insight into the relevance of sound visualisations in the context of data analysis and interpretation; - To enable students to select the most appropriate methods of data analysis and visualisation; - To familiarise students with visual thinking and with eliciting and addressing research questions from a visual perspective; - To make students aware of the elements of effective and meaningful visual designs; - To enable students to develop data-related visual and communicational skills. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Recognise the principles underlying an effective analysis and visual representation of data; - Identify the most appropriate strategies for data visualisation; - Explain how to critically analyse and evaluate data visualisation in light of purpose and audience; - Demonstrate how data visualisations can be proficiently used as a means toward data interpretation; - Produce meaningful and effective visualisations; - Select the proper procedures to address theory-driven research questions via visual aids; - Develop visual thinking (application, critique, evaluation, and generalisation). 2. Skills: By the end of the study-unit the student will be able to: - Carry out basic capturing, storing, and pre-processing operations of data; - Conduct exploratory analyses using visualisations; - Produce different types of data visualisations according to the type of data and analytical goals; - Craft visual presentations of data for an effective communication; -I dentify how visualisations can be designed in order to highlight specific information relevant to the tasks at hand; - Perform different types of visual analysis to address specific research questions; - Understand and select appropriate strategies to understand trends, outliers, and patterns in data. Main Text/s and any supplementary readings: Main Texts: - Healy, K. (2019). Data Visualization. A Practical Introduction (1st ed.). Princeton University Press. - Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten (Second ed.). Analytics Press. - Kirk, A. (2019). Data Visualisation: A Handbook for Data Driven Design (2nd ed.). SAGE Publications Ltd. - Rahlf, T. (2020). Data Visualisation with R: 111 Examples (2nd ed. 2019 ed.). Springer. - Rowntree, D. (2018). Statistics without Tears: An Introduction for Non-Mathematicians. Penguin UK. - Swires-Hennessy, E. (2014). Presenting Data: How to Communicate Your Message Effectively (1st ed.). Wiley. - Wilke, C. O. (2019). Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures (1st ed.). O’Reilly Media. Supplementary readings: - Dick, M. (2020). The Infographic: A History of Data Graphics in News and Communications (History and Foundations of Information Science). The MIT Press. - Chang, W. (2018). R Graphics Cookbook: Practical Recipes for Visualizing Data (2nd ed.). O’Reilly Media. - Spatz, C. (2019). Exploring Statistics. Macmillan Publishers. - Zumel, N., & Mount, J. (2019). Practical Data Science with R (2nd ed.). Manning. |
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| STUDY-UNIT TYPE | Lecture | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Gianmarco Alberti |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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