Data Visualisation

(3 Credit Hours) · Undergraduate

In this unit, students develop an understanding of scientific computing and modelling, analysis, problem solving and visualisation. They understand the approach of computational modelling; have strong programming skills in Excel, MATLAB and Mathematica in data analysis modelling; understand limitations and uncertainty in models; devise and implement computational models; analyse data and hypotheses; are aware of reliability and correctness; and are able to perform simulation and testing. Hong Kong: This course will introduce visualization techniques for data from everyday life, social media, business, scientific computing, medical imaging, etc. The topics include human visual system and perception, visual design principles, open- source visualization tools and systems, visualization techniques for CT/MRI data, computational fluid dynamics, graphs and networks, time-series data, text and documents, Twitter data, and spatio-temporal data. The labs and the course project will give students hands-on experience to turn their data into beautiful visualizations.On successful completion of the course, students will be able to: 1.Understand human visual system, color, design principles, and core techniques of data visualization. 2.Learn to understand domain problems and needs of end users. 3.Learn to design, implement and evaluate a visualization system. 4.Communicate effectively with domain experts, general public, and different stakeholders in government, academia and industry. 5.Analyze social impact and responsibilities as well as possible ethical, legal, security and privacy issues.