This course introduces the essential mathematical, statistical, and data-handling concepts required to work effectively in football analytics. Learners will build a solid foundation by exploring measures of central tendency, variability, probability distributions, standard deviations, and confidence intervals, the core concepts that underpin all analytical reasoning in sport. Through football-specific examples, the course explains when to use different estimators, how to interpret uncertainty, and why choosing the right distribution is critical when modeling performance and match events.
Must-Know Concepts - Basic requirements for data analysis
Must-Know Concepts - Basic requirements for data analysis
This course is part of Maximum Performance and Technology in Sports Specialization
Instructor: Marisa Sáenz
Included with
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Apply key statistical concepts (means, distributions, variability) to football data.
Understand data types (counting, event, tracking, skeletal) and how they shape analysis.
Use Python, APIs, and visualization tools to process and communicate football insights.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
December 2025
Assessments
8 assignments
Taught in English
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This course is part of the Maximum Performance and Technology in Sports Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Build a solid foundation in the mathematical concepts that are crucial for data analysis. This module breaks down various types of averages, explores common data distributions, and demystifies important measures like standard deviation and confidence intervals—empowering you to interpret data with clarity and precision.
What's included
6 videos4 readings2 assignments
Explore the diversity of data in today’s analytical landscape. You’ll learn how to distinguish between basic counting and GPS data, understand the nuances of event data from different providers, and discover advanced tracking and skeleton data that add rich context to sports analysis.
What's included
6 videos3 readings2 assignments1 discussion prompt
Get hands-on with the tools and techniques that bring data to life. This module introduces you to gathering data through APIs and web scraping, processing it using Python and essential data formats, and transforming raw numbers into compelling visuals with platforms like Tableau, Matplotlib, and Power BI.
What's included
6 videos3 readings2 assignments1 discussion prompt
Transform complex analyses into clear, actionable messages. Learn to identify the purpose of your analysis, strike the right balance between detail and simplicity, and use thoughtful chart design to ensure your audience grasps the insights—without being misled.
What's included
6 videos3 readings2 assignments1 discussion prompt
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor
Explore more from Data Analysis
Real Madrid Graduate School Universidad Europea
Real Madrid Graduate School Universidad Europea
Real Madrid Graduate School Universidad Europea
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