The discipline of Science of Cities examines relationships between the physical form of cities and the social, cultural, economic, technological and spatial processes that give rise to this form. As technology evolves and changes, so do the ways in which we make and think about our cities. In this era of unprecedented and fast-accelerating changes, digital technologies are reshaping the ways in which we measure, sense, conceive of, design and plan for our cities. As a result, we collect and store large amounts of data on every aspect of the urban environment, but it is as yet unclear how this data can be used to inform evidence based planning and urban management. In particular, it is unclear how these quantitative methods and data driven frameworks may be best leveraged for planning and designing just, equitable, sustainable, liveable, and affordable cities. This unit of study will introduce the principles of science of cities and the tools, methods, algorithms and techniques on big urban data that enable transformative ways of thinking about, designing and planning for a fast urbanizing world. Fundamentals of programming with big urban data will be introduced through the Python programming language (Jupyter Notebooks) and open source Geographic Information Systems (GIS) software. Emphasis will be placed on developing understanding of urban structure and fast and slow dynamics shaping this structure, and on the use of data to develop performance indicators for cities, in particular targeting the spatial and temporal measurement accessibility, affordability, segregation, displacement, social exclusion, and disadvantage. This transdisciplinary unit of study will be relevant for designers, planners, engineers, geographers, economists, physicists and data scientists interested in modelling urban systems.