MHS – HP Data Courses

Examination of performance technologies and monitoring systems emphasizes their role in supporting data-informed decision-making in human performance settings. Learners interpret monitoring data to evaluate readiness and performance status, implement standardized data collection protocols, and determine the validity, reliability, and feasibility of performance tools. Emphasis is placed on ethical considerations, data privacy, and the responsible use of technology in performance and return-to-play contexts.  

This course examines sport and performance demands across athletic and active populations to inform assessment and planning. Learners evaluate assessment strategies to identify performance strengths and limitations, apply needs analysis principles to establish benchmarks and goals, and integrate interdisciplinary input into performance planning. Emphasis is placed on effectively communicating assessment findings to support performance decision-making.  

This course develops the application of data analysis to support decision-making in human performance settings. Learners evaluate data quality and limitations, apply analytical techniques to performance and injury scenarios, and interpret findings to guide training and performance decisions. Emphasis is placed on translating data into clear, actionable insights for stakeholders.  

This practicum provides applied experience in sport science and human performance settings. Learners apply performance principles in real-world or simulated environments, using athlete or client data to inform decision-making and evaluate interventions. Emphasis is placed on integrating monitoring, assessment, and data strategies into practice while collaborating with interdisciplinary stakeholders to support performance outcomes.  

This capstone course integrates sport science, performance data, and evidence-informed decision-making in applied settings. Learners analyze complex performance scenarios using real-world datasets to develop and justify actionable recommendations. Emphasis is placed on translating data into meaningful insights, communicating findings to stakeholders, and supporting decisions within interdisciplinary performance environments.