Master of Science in Applied Sports Science Analytics

This course examines the role that science and evidence-based practice plays in enhancing human performance, in high performance settings. A distinctive feature of the subject is its focus on developing student’s knowledge, decision-making, and applied research skills required to plan and deliver evidence-based interventions for athletes to enhance health and performance. There is a strong focus on preparing graduates with relevant practical skills that are used by high performance sport practitioners. Students will be taught to translate advanced concepts to professional practice through the application of critical thinking, independent learning and effective communication.

This course applies statistical learning methods for data mining and inferential and predictive analytics to the field of sports science. The course also introduces techniques for exploring and visualizing data, assessing model accuracy, and weighing the merits of different methods for a given real-world application. Emphasis is placed on the analysis, interpretation, and communication of data essential in creating a toolset for transforming large, complex sports science informatics datasets into actionable knowledge. Pre-requisite: Data Analytics

This subject examines the role of resistance training, aerobic/anaerobic conditioning and other contemporary techniques that are used to prepare high performance athletes. Students develop a strong understanding of the fundamental principles which underpin the training response, as well as learn to critically evaluate emerging techniques within the strength and conditioning field. Additionally, this subject equips students with an in-depth understanding of training prescription that can be utilized in any field that prescribes exercise (e.g. exercise physiology, rehabilitation, and injury prevention). The strategies and interventions explored in this course focus on enhancing performance, and reducing the risk of injury for high performance athletes. Students will engage in problem solving and critical thinking activities for independent learning and the effective translation of expertise into practice.

This course covers the concepts of information technology used in manipulating, storing, and analyzing big data. Students gain an understanding of the tools used for statistical analysis, Python, and several machine learning algorithms for application in an industry setting. Emphasis is on designing, implementing, and developing machine learning algorithms. Focus is placed on interpretation and visualization of results.

The course involves a study of joint structure, joint function, and the biomechanical principles underlying the kinetics and kinematics of human motion, including normal gait and human movement. Emphasis is placed on the interaction between biomechanical and physiological factors in musculoskeletal and neuromuscular function, and the application of kinesiological principles to clinical rehabilitation practice.

This course provides an overview of foundations of research design and the uses and interpretation of results. Content includes: reviewing the literature, developing research problems/questions; hypothesis testing, experimental, quasi-experimental and other research designs; and evaluating research studies as they relate to evidence-based practice in the health professions. The intended outcome is to familiarize students with the evidence-based guidelines associated to clinical outcomes and evidence-based practice.

Through the completion of a research project, on a topic within the field, this course serves as an essential outcome component to augment the professional development and new learning that occurs in didactic course work and demonstrates the ability of the graduate to make significant contributions to their professions. Pre-requisite: must be taken in final semester of program. Prerequisite: RSCI780

An introduction to the ethical and social consequences of collecting, curating, and analyzing data in academia, public and private contexts. A socio-technical stance is taken in unpacking issues of algorithmic biases, fairness, transparency, and accountability. Additionally, students develop a strong understanding of responsibilities and issues associated with the culture of the sports science field. Students will develop an understanding of the importance of effectively and accurately communicate data in sports environments. Students will practice building effective work environments and develop innovative training principles through the use of key sport science concepts.

This is an introductory course in design and evaluation of interactive visualizations for data analysis. Topics include human visual perception, visualization design, interaction techniques, and evaluation methods. Students develop projects to create their own web-based visualizations and develop competence to undertake independent research in visualization and visual analytics. Pre-requisite: Data Analytics

Applied Data Analytics with R