Data Literacy Development

About Practitioner Development in Data Literacy

This page is designed to provide direction, materials and information to assist you in advancing your data literacy competencies as a sport practitioner. Content has been organized and developed by the HPAC Data Literacy Working Group.

Contributing Members:

• Ryan Brodie (Chair) – Canadian Sport Institute Pacific
• Dr. Sylvain Gaudet, PhD – Institut national du sport du Québec
• Dr. Dave Clarke, PhD – Simon Fraser University
• Ryan Atkinson – Canadian Sport Institute Ontario
• Dr. Marc Klimstra, PhD – Canadian Sport Institute Pacific
• Dr. Erik Groves, PhD – Canadian Sport Institute Alberta
• Ming-Tsai, PhD – Canadian Sport Institute Pacific

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Our Purpose

Sport Scientist Canada recognizes the importance of data literacy competencies in the development pathway of HP sport practitioners. Our primary purpose is to define a Data Literacy Development Pathway for HP sport practitioners by offering opportunities and guidance to upskill practitioners currently involved within the HP Canadian sport system, in data literacy.

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What is Data Literacy?

Data literacy includes the ability to read, work with, analyse and present data-driven arguments. High performance (HP) sport is becoming increasingly data driven, with more emphasis being placed on using data to assess performance, draw conclusions and make decisions regarding athlete programming. As technology continues evolving, the role of data science in high performance sport will keep increasing. Capitalizing on data science will be fundamental to the success of our high-performance athletes. As such, data literacy is a vital skill for sport practitioners.

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Data Science in High Performance Sport

Data science is a new, popular, and evolving field, making it difficult to define precisely. It involves applying statistical techniques to large or complex data sets to answer scientific questions, requiring skills in computation and applied statistics. This involves skill sets in the areas of computation, applied statistics, machine learning and data engineering. Teams of high performance sport science practitioners apply these skills with existing sport science expertise in sport analytics, combining data management, analysis, reporting and visualization to answer sport specific performance-relevant questions, applying scientific rigor.

Skill areas generally relevant to the practice of data science in a high-performance sport environment include (Clarke, 2020):

Data Science Domain	Skills
Data Management	Data acquisition and secure storage Data quality evaluation and control, data cleaning Standardization
Analytics	Validation Statistical inference, modeling, prediction HP-sport-specific analytics, e.g., Podium Pathway, probabilities
Visualization and Reporting	Plotting results Written reports, oral communication Interactive dashboards

Data Literacy Development Pathway

Data literacy competencies encompass a wide range of knowledge and skills necessary to work effectively with data, from basic skills such as data awareness, data gathering, and data exploration to advanced skills like data discovery, data management, and data modeling. Depending on your role in the Canadian high-performance sport system, educational background, and personal interests, you may need to develop certain competencies more than others. The Data Literacy Development Pathway is divided into three main steps based on the level of data literacy expertise.

1 > Professional Development	2 > Enhanced Focus for Practitioners	3 > Advanced Certification
Resources and modules for improving data literacy and data science competencies.	Resources, assessment, and review processes to ensure existing practitioners possess or can upskill to the necessary skills for sophisticated data analysis projects.	Individuals who meet academic or professional standards can become certified as Sport Data Science Practitioners.
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Professional Development for Practitioners and Stakeholders

Science and medicine practitioners develop varying levels of data literacy during their academic degrees. The SSC Data Literacy Modules are available to members in order to promote data literacy among practitioners and stakeholders. Keep an eye on this page for future updates, as additional resources developed for this initiative will be made available here.

These modules, and the high-performance certification process for all practitioners contribute to competencies in data management and analysis at a foundational level. Practitioners looking to target professional development to the core competencies of the High Performance Certification (HPC) process, specifically those focusing on data management and evidence-based decision, can view the HPC Competency Framework Here.

In addition to these educational modules, the Sport Data Makerspace is a resource available to practitioners interested in reading about and discussing ongoing projects in data analysis programming workflows, signal processing, sport sensors and other topics in sport data. Initially started as an initiative to further the development of sport sensors specifically, it has broadened to include general programming and analysis topics.

 

Enhanced Focus in Data Science for HPC Practitioners

Gaining certification as a high-performance practitioner within Sport Scientist Canada’s HPC framework requires mastering a comprehensive set of competencies. This includes not only a thorough understanding of data collection and management—encompassing ethical practices and data protection—but also foundational skills in statistical analysis, effective data visualization, and clear reporting.

Coming in 2025, the Data Science Area of Focused Competency (AFC) will offer practitioners already certified in other areas a chance to advance their qualifications in data science. This AFC ensures that they possess the necessary skills for sophisticated data management, analysis, and communication. Key competencies include advanced analytic and modeling techniques, handling diverse and complex datasets, and proficiency in higher-level programming languages. Building on the foundational skills covered by the High-Performance Certification (HPC) in Data Science, this AFC aims to further professional development and elevate practitioners’ expertise in data-driven fields.

 

Advanced Certification for Specialist Data Science Practitioners

Certification as a data science practitioner requires further academic and professional credentials, which are described in About High Performance Certification. Qualifications include an MSc or PhD in fields such as Statistics, Computer Science, Engineering, Physics, Mathematics, Data Science, and Analytics, with the consideration of an equivalent graduate degree for candidates who have a bachelor’s in data science. Education should encompass applied statistics, database interfacing, statistical modeling, machine learning, and high-performance computing. It is recommended that candidates be affiliated with a professional association relevant to statisticians, computer scientists, or data scientists.