DSCI351 – Sports Analytics

Course Breakdown
3 Intense Days
7 Hours per Day (Split into two 3.5-hour sessions)
Total: 21 Hours

Learning Path Visual

Here’s your journey from data novice to performance strategist:

Day 1 – Foundations & Data
Understand the evolution of sports analytics, learn to collect and clean data, and apply basic statistics using Excel.

Day 2 – Advanced Analytics & Modeling
Dive into regression, predictive analysis, and machine learning with R and Python to forecast player and team performance.

Day 3 – Strategy, Ethics & Projects
Apply decision-making frameworks, explore the business impact of analytics, and deliver your final sports analytics project.

Course Overview

Sports Analytics blends data science with sports management to reveal how numbers shape every aspect of athletic performance, team success, and fan engagement.

In just 3 days, you’ll explore data collection, visualization, and modeling techniques using Excel, R, and Python. You’ll work with real-world datasets to uncover insights about player efficiency, strategic decisions, and market trends. You’ll also examine the ethical and commercial sides of analytics in the global sports industry.

Whether you aim to work in data analytics, coaching, sports marketing, or management, this course equips you with practical tools and frameworks to make informed, evidence-based decisions in sports.

What’s Inside Each Day

Day 1 – Foundations & Data

• Explore the evolution and impact of sports analytics.

• Learn key statistical concepts for analyzing player and team data.

• Practice Excel-based analytics: cleaning, summarizing, and visualizing sports data.
  Framework: Data ➔ Insight ➔ Performance

Day 2 – Advanced Analytics & Modeling

• Apply regression and predictive analytics to evaluate performance.

• Use R for statistical modeling and Python for machine-learning predictions.

• Build a win-probability or player-efficiency model.

Day 3 – Strategy, Ethics & Projects

• Examine ethical issues in player tracking, data privacy, and fairness.

• Explore decision analysis and optimization for team management.

• Present a final project integrating data visualization, prediction, and strategic insight.

Course Goals

By the end of this program, you’ll be able to:

• Collect, clean, and visualize sports data using Excel.

• Apply R and Python to build and interpret predictive models.

• Evaluate player performance and team strategy through analytics.

• Assess ethical, social, and business impacts of data in sports.

• Deliver a final sports analytics project using real datasets.

Who Should Take This Course?

This course is perfect for:

• Business and IT students interested in data-driven sports applications.

• Sports management professionals seeking analytical decision-making skills.

• Coaches and analysts wanting to integrate technology into performance strategy.

• Entrepreneurs exploring opportunities in sports technology and analytics.

Class Reference: DSCI 351
Form Updated on: 06/10/2025 (Version 1)
Last Modified on: 06/10/2025

Program Information:
This program is continuously updated to reflect new trends in sports technology, wearable data, predictive analytics, and global sports business practices.

Brief pédagogique en français

DSCI351 – Sports Analytics est présenté ici en version synthétique française afin que les équipes pédagogiques puissent évaluer rapidement l'intérêt du module.

Le module s'inscrit dans la famille Analytics et intelligence artificielle. Il peut être adapté au calendrier de l'école, au niveau Tous niveaux, au volume horaire 21 h et aux modalités d'évaluation prévues.

Objectif d'intervention

Ce module vise à relier les outils data, IA et automatisation à des usages professionnels concrets.

Livrables et activités possibles

• cas d'usage, prompts, scénarios d'automatisation ou analyses data
• évaluation critique des résultats, limites et risques
• communication claire des choix techniques et business

Adaptation école

LC peut ajuster le déroulé, la langue d'enseignement, les supports, les exercices et les critères d'évaluation selon la promotion, le diplôme, le niveau d'autonomie attendu et les contraintes de planning.

Pour une version détaillée du syllabus en français, LC confirme le programme final après cadrage du niveau, des heures, du calendrier et des livrables attendus.