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This module is split into two parts: Part A focuses on the technical implementation of data visualisation.ÌýPart B focuses on the critical thinking of data visualisation.ÌýCombined, the lectures will build up your full understanding of the contemporary thinking and techniques of data visualisation.ÌýTo undertake this course, students should have a basic understanding of the Python programming language and some experience with Pandas.

Part A of this module offers a technical introduction to data visualisation, starting with analysis and visualisation using Python Jupyter notebooks (with Pandas,Ìý Matplotlib and Plotly) and showing how the results of this analysis can be used in a simple web presentation. As well as showing how to tame your data using notebooks it will cover the basic web-dev skills needed to transfer these results to the web using static charts (Matplotlib and Seaborn) or dynamic, interactive charts, the latter using Python's Plotly library and Datawrapper, an online app.

Part B of this module equips students with a theoretical and practical understanding of data visualisation, covering the full spectrum of contemporary capabilities required to master this subject. You will learn about the role of design thinking in the visual communication of data, following a workflow process that will enhance the efficiency and effectiveness of your creative choice-making. You will learn about the importance of developing journalistic instincts, the vital recognition of contextual influence, and the intricacies of working with data to form the foundation of your analytical flair. This side of the module shines a light on the human side of communicating with data, exposing the nuances, the trade-offs, and the imperfections, but also giving you the confidence to embrace these uncertainties rather than seek to escape them.