This module introduces visual data analysis from a mathematical point of view. It is designed as a compact 2.5-credit module with 2 hours of lecture and 1 hour of tutorial per week. The module emphasizes descriptive statistics, uncertainty, visual reasoning, and the use of Python as a mathematical exploration environment. Students learn to choose and interpret visualizations for different data types, communicate mathematical ideas through figures, and maintain structured notes using Markdown and LaTeX-style notation. AI tools may be used responsibly to support code checking, mathematical expression, visualization choices, and explanation, while students remain responsible for mathematical judgment and interpretation. The module is designed for general education purposes to cultivate students' data literacy, enabling them to understand the importance of data and its role in informed decision-making. This foundational competency is critical for a smooth transition to university and, more importantly, for making sound professional decisions in the real world.
A Use descriptive statistics and basic uncertainty concepts to summarize and interpret data. B Explain data visualization as a form of mathematical communication and exploration. C Select appropriate visual strategies for categorical, numerical, bivariate, time series, spatial, network, and multivariable data to present information in professional context D Apply visualization techniques to make informed decision in the real world. E Write structured mathematical notes using Markdown, tables, code blocks, and LaTeX-style notation.
Teaching is delivered through weekly lectures and tutorials. Lectures introduce the mathematical and visual ideas behind each chart family, including data type, graphical encoding, comparison, uncertainty, and ethical interpretation. Tutorials provide guided practice in Anaconda/Jupyter Notebook, Python plotting libraries, Markdown note-taking, and compact visualization exercises. The first two weeks are used for environment setup, Anaconda installation, Jupyter Notebook use, Markdown, LaTeX-style notation, and reproducible notebook habits. Subsequent teaching follows chart families inspired by the Python Graph Gallery, focusing on how to select, construct, and interpret graphs rather than on broad programming content.