Courses
The certificate follows a carefully designed sequence: mathematical foundations → programming and visualization → industry context → capstone execution. Each course prepares you for the next while building standalone skills.
MTH 801: Machine Learning Algorithms
The program’s foundational course builds the mathematical and computational scaffolding students need to engage seriously with sports analytics. Working through regression, classification, and dimensionality reduction methods, students develop both theoretical understanding and hands-on experience applying these tools to sports-related questions in R. The emphasis is on understanding why methods work, not just executing them — a distinction that pays dividends in every subsequent course.
Course Details
What You’ll Learn
Understand the mathematical foundations underlying machine learning methods: linear regression, principal component analysis, support vector machines, and the perceptron algorithm (a precursor to neural networks). This isn’t a “how to use the library” course—you’ll understand why algorithms work and when they fail.
Why It’s Different
- Math-first approach: Derive algorithms before implementing them
- Theory meets practice: Computational problems paired with theoretical understanding
- Foundation for everything else: The methods you learn here appear across sports analytics applications
Typical Workload: 3 take-home assignments across semester
STT 832: Statistical Learning and Data Mining
Students develop fluency in R as an analytical environment, with particular attention to transforming raw data into clear, compelling visualizations that can inform decisions. The course covers data cleaning, exploration, and the construction of publication-quality graphics, building the kind of practical programming confidence that separates analysts who can execute from those who can only supervise. By the end, students are equipped to wrangle messy real-world data and tell coherent stories with what they find.
Course Details
What You’ll Learn
Master R programming, statistical modeling, and data visualization techniques essential for sports analytics. Focus on practical implementation of statistical methods for real-world sports data challenges.
Why It’s Different
- Hands-on R programming: Build fluency in the language of sports analytics
- Real sports datasets: Work with actual team and player performance data
- Visualization focus: Create compelling charts and dashboards
- Statistical rigor: Understand when and why to use different analytical approaches
Prerequisites
Typical Workload: Programming assignments throughout the semester
MTH 803: Sports Analytics Practicum
Rather than adding more methods to the toolbox, this course asks students to engage directly with the people using analytics inside sports organizations. Through a structured guest speaker series spanning team operations, league offices, player representation, and sports media, students practice the art of strategic inquiry — learning to ask questions that reveal how decisions actually get made, not just how they’re supposed to. The conversational format doubles as professional networking experience, and the relationships students build here often extend well beyond the semester.
Course Details
What You’ll Experience
Guest speakers from across the sports analytics ecosystem share their real-world experiences, challenges, and implementation strategies. This isn’t just theory—it’s how analytics actually works in teams, leagues, and agencies.
Why It’s Different
- Guest speakers: Front office analysts, player agents, league researchers, media analysts, scouts, and technical experts
- Real organizational challenges: Understand politics, constraints, and opportunities
- Interactive discussions: Ask questions about actual implementations
- Career pathway insights: Learn how professionals made the transition to sports analytics
What Makes This AI-Forward
Speakers address how AI is changing their work, what skills are most valuable, and how to stay relevant as the field evolves.
Integration with STT 834
Guest speakers provide context for capstone projects, helping students understand how their analytical work fits into organizational decision-making.
Prerequisites: MTH 801, STT 832
Format: 60-90-minute sessions with professionals throughout the spring semester
STT 834: Sports Analytics Capstone
The capstone is where everything comes together: students independently scope, execute, and present a portfolio-quality analytics project addressing a real personnel or revenue decision from an organizational perspective. Deliverables go beyond a written report to include a documented GitHub repository and a professional project website built with GitHub Pages — the kinds of artifacts that actually move the needle in a job search. Weekly standups, peer critique, and individual instructor support keep projects on track while building the collaborative habits that define effective analytics teams.
Course Details
What You’ll Build
A complete analytics project addressing personnel or revenue decisions from an organizational perspective. You’ll produce: a written report, public presentation, documented GitHub repository, and professional GitHub Pages website.
Why It’s Different
- Integrated with MTH 803: Four-week intensive launch, then maintain momentum during speaker series
- Portfolio focus: Website designed to show recruiters, not just satisfy course requirements
- Weekly standups: Professional workflow practice with peer problem-solving
- Iterative pivoting encouraged: Adjust research questions as data realities emerge
What Makes This AI-Forward
Explicit guidance on using AI for debugging, code scaffolding, and exploration—but you must explain every analytical decision, methodology choice, and code implementation. During standups and presentations, expect questions about any aspect of your work.
Example Projects (from past cohorts)
- Impact on player performance when transferring NCAA men’s basketball conferences
- Contract financial modeling in the Premier League
- Designing a Stuff+ model for NCAA pitching
Prerequisites: MTH 801, STT 832, MTH 803 (concurrent)
Deliverables
- In-class presentation (25%; Finals Week)
- Finals Week: Public presentation (25%)
- Portfolio website (30%)
- Weekly standup participation (10%)
- Class participation (10%)