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This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 9: Using High-Frequency Data to Improve Water Quality, a ~3-hour educational module for undergraduates. In recent decades, there have been substantial improvements in our ability to monitor water quality in real time using sensors that measure variables at a high frequency (e.g., every few minutes). These high-frequency data have tremendous potential to inform drinking water management by providing real-time information to water treatment plant operators regarding water quality. Moreover, in addition to directly informing water management decision-making, collection of high-frequency water quality data has enabled recent development of water quality forecasts, or predictions of future water quality conditions with uncertainty. Often, water quality forecasts are produced with the goal of informing and improving water treatment and management by giving managers a pre-emptive warning about potential water quality impairment. To introduce water treatment students to the use of high-frequency data and forecasts to improve water quality, we created a short (one- to three-hour) module which develops key skills in high-frequency data and forecast visualization and interpretation. These forecasts are applied to drinking water treatment scenarios, using data from the Virginia Reservoirs Long-Term Research in Environmental Biology (LTREB) program. This module was created as part of a virtual, asynchronous curriculum for community college students training to become drinking water treatment plant operators, and could also be taught in high school or introductory undergraduate environmental science and natural resource management courses. The module is designed to be flexible for use in-person, hybrid, and virtual, asynchronous course formats. Students complete module activities using an R Shiny web application which can be accessed from an internet browser on a computer. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module9/. A GitHub repository is available for the R Shiny application code (https://github.com/MacrosystemsEDDIE/module9), and the code repository has been published with a DOI to Zenodo (https://zenodo.org/doi/10.5281/zenodo.13835235). This data package includes open source versions of module introductory lecture slides, a student handout, and an instructor manual which can be used to teach the module. Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module9.html).