| 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 not only equipping students with the necessary tools to visually represent data but also providing a solid foundation in basic statistical analysis. Students will explore real-world examples that will guide them to frame research questions both visually and statistically. This combined approach ensures that the student can understand the data at its core and then represent it in the most effective way. Hands-on use of free software further facilitates a comprehensive learning experience. By the end of the study-unit, student will be able to locate, understand, and visually portray patterns in raw data, while also making sound statistical judgments. They will be adept at considering the type of data, the goals, the audience, and at critically evaluating the design of any data visualisation and its underlying statistical implications. Study-Unit Aims: - To elucidate the intrinsic relationship between data visualization and statistical analysis; - To provide an insight into the relevance of sound visualisations in the context of data analysis, interpretation, and statistical testing; - To enable students to select the most appropriate methods of data analysis, statistical interpretation, and visualisation; - To familiarise students with the nuances of data variability, relationships between variables, and foundational statistical tests; - To familiarise students with visual thinking and statistical reasoning; - To ensure students master the elements of effective visual designs and their underlying statistical significance;- To enable students to develop data-related visual and communicational skills; - To empower students with data-related visual, analytical, 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, statistical evaluation, and visual representation of data; - Identify and apply foundational statistical concepts like sample-population relationship, data variability, confidence intervals, correlation, association, and hypothesis testing; - Demonstrate how data visualisations, complemented by sound statistical reasoning, can enhance data interpretation; - Evaluate and select the proper procedures to address research questions using both visual aids and statistical tests; - Select the proper procedures to address theory-driven research questions via visual aids; - Develop holistic data understanding through combined visual and statistical thinking. 2. Skills By the end of the study-unit the student will be able to: - Carry out data capturing, pre-processing, and statistical descriptions; - Conduct exploratory analyses using visualisations and foundational statistical methods; - Produce and interpret data visualizations such as scatterplots, boxplots, and cross-tabulations in tandem with their statistical implications; - Understand relationships between variables, including categorical variables and statistical tests like chi-squared tests; - Communicate data-driven insights effectively, merging visual representation with statistical validation. 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 2024/5. It may be subject to change in subsequent years. |
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