Data analytics is the science of drawing insights from raw data for human consumption. It aims to reveal trends and metrics that would otherwise be lost in the mass of information. The gained insights can then be used to optimize systems or processes to increase their overall efficiency. This course provides fundamental knowledge of data analytics applications in the energy systems field. The motivation and potential advantages of implementing advanced data analytics for typical energy systems will be introduced. Basic concepts and the procedures of the typical descriptive, diagnostic, predictive, prescriptive analytics for general energy system problems will be discussed. Recent literature that covers a wide range of topics related to energy systems data analytics such as peer-to-peer energy trading and its blockchain implementation, power system cybersecurity and data injection, electrical load forecasting, building energy management, renewable generation modeling, and penetration of electric vehicles. By dealing with huge amount of data from energy information systems, many benefits can be brought to the existing infrastructure and improve customer service as well as the social welfare in the era of big data. Python, Pandas, Scikit Learn, Keras/Tenserflow will be introduced as computational tools via hands-on exercises. This is a research-oriented course that allows students to engage in research activities that leads to academic publications. 3 Credits. Prerequisites: SSIE 505 Applied Probability and Statistics; or Equivalent. Offered in fall or spring