Sport Data Analytics (Advanced Certificate)

Program Director: 

Sana Spector, Professor of Mathematics & Statistics and Data Analytics
Contacts: 716-888-2845 or spektors@canisius.edu

The Advanced Certificate in Sport Data Analytics is a specialized, fully online program designed for professionals seeking to enhance their skills in applying data analytics within the sports industry. Focusing primarily on sports performance analytics and sports betting analytics, the program equips students with the expertise to analyze and optimize performance while maintaining ethical standards in data use and decision-making. This 12-credit curriculum offers a comprehensive foundation in data manipulation, analysis, and visualization to support data-driven strategies in sports.

Admissions Requirements

• Students from any undergraduate major are welcome to apply, as long as they have acquired a bachelor's degree prior to the start of classes.
• Cumulative GPA of 2.8 or higher.
• Successful completion  of an introductory statistics course (e.g., MAT 131, MAT 141, DAT 211, or equivalent) is required to ensure readiness for advanced analytical coursework.
• Students may apply at any time. We have rolling admissions.
• Student preparation and background are used to determine if some foundation courses may be waived.

Materials to be Submitted

• Online Application, with personal statement
• An official transcript from each college attended
• Resumé
• Official GRE or GMAT score (optional)
• Letters of Recommendations (optional)

Policies

Academic Standing

The Sport Data Analytics program follows the College of Arts and Sciences on students' academic standing.

Matriculation and Continued Program Enrollment

The Sport Data Analytics program follows the Canisius University policy for matriculated students that expects students to maintain a continuous program of academic work.

Registration and Credit Hours

Sport Data Analytics students must be registered for at least 4.5 credits per semester to maintain eligibility for financial aid (if they are eligible).  A full load is at least 9 credit hours.  No student may register for more than 12 credit hours in any semester.

Curriculum

Learning Goals and Objectives

Upon successful completion of the Advanced Certificate in Sport Data Analytics, students will be able to:

1. Apply Advanced Data Analytics in Sports Contexts
   Utilize statistical methods, machine learning techniques, and data visualization tools to analyze and interpret sports performance and betting data.

2. Create Effective Data Visualizations
   Design and develop interactive, insightful visualizations and dashboards using R and Tableau to support data-driven decision-making in sports organizations.

3. Analyze Sports Performance Data
   Evaluate player and team performance metrics using real-world data, including player tracking and biomechanical data, to inform training, strategy, and injury prevention.

4. Build Predictive Models for Sports Outcomes
   Develop and assess predictive models for forecasting sports events and betting outcomes, with attention to accuracy, interpretability, and practical application.

5. Demonstrate Ethical Data Practices
   Identify and apply ethical standards in sports data analytics, including responsible data use, privacy considerations, and fair decision-making practices.

6. Communicate Data-Driven Insights Effectively
   Present complex analytical findings clearly and persuasively to diverse stakeholders, including coaches, analysts, management, and betting professionals.

7. Integrate Multiple Data Sources for Strategic Analysis
   Combine and manage varied sports-related datasets to create comprehensive analytical solutions that address real-world challenges in the sports industry.

Courses