This tutorial will prepare attendees to analyze PMU (synchrophasor) data for research and practical applications. The tutorial will provide hands-on experience with state-of-the-art tools for digesting and visualizing high-frequency time series data, and for exploring novel applications. PMU data give empirical evidence of physical phenomena that happen on time scales unobservable to conventional sensor networks. Effective use of PMU measurement data can unlock novel opportunities for using Artificial Intelligence (AI) to extract insights into the condition of the grid. Making these insights accessible to decision makers in real-time has already begun to radically change best practices in grid operations, maintenance, and planning. This tutorial will provide attendees with the context and skills they need to leverage AI methods to begin using PMU and other high frequency data in their own work. The course will begin by teaching fundamental concepts from power systems engineering, and their relation to PMU measurement data. This talk will provide context necessary for both newcomers and domain experts to begin analyzing and interpreting PMU data. The course will go on to describe the data analytics program at Dominion, where streamlined access to PMU data has unlocked unexpected opportunities to improve decision-making processes related to grid operations, maintenance, and planning. Finally, we describe how companies that are successful at leveraging data and AI have radically changed the way they do business. We discuss examples from other sectors, such as Amazon and Google, and will share an outlook for similar transitions in the energy sector. Attendees will gain hands-on experience working with PMU data and state-of-the-art computational tools designed to facilitate the analysis and interpretation of big data. The hands-on portion of the session will use the National Infrastructure for AI on the Grid (NI4AI) powered by PingThings’ PredictiveGrid Platform and will provide real-time support for participants to gain API access to PMU and other data that are publicly hosted on the platform. Talks will include an interactive exercise for participants to familiarize themselves with the visualization capabilities of the platform, and to access the data on their personal computers using the Python API. Talks will include live coding demonstrations of two use cases for PMU data, to examine voltage sag events and explore the relationship between solar generation and voltage or frequency on the grid. Participants are requested to bring personal computers that they may follow along and replicate analytics on their own devices.