Modern cities depend on data flows that connect users and infrastructure. Thus, data science skills are critical for design and operation of smart cities. The abundance of data, and statistical analysis and machine learning algorithms for utilizing the data are expected to significantly improve decisions about how urban infrastructure and its environment are maintained and built. This course teaches basic, readily applicable data analytics, statistical methods, and machine learning algorithms that are useful for exploiting data obtained via crowd-sensing and remote sensing technologies within transportation, environmental, building, and power grids systems. The course will be taught in four modules: mobility and transportation, building energy systems, extreme events and urban resilience, and climate change and environmental variability. Throughout the course, students will learn to use real data to solve smart city application problems via basic statistics and machine learning techniques.