Must-Know Concepts - Basic requirements for data analysis

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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

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4 modules

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

4 modules

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

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

Beyond statistics, learners discover the ecosystem of football data itself, including data on counts, GPS tracking, event data, and skeletal tracking, and understand how each type is collected, structured, and used in professional analysis. The course also introduces key analytical tools such as APIs, web scraping, Python, data structures, and visualization principles using Tableau, Power BI, and Matplotlib. By the end of this course, learners will be equipped with the mathematical intuition, technical fundamentals, and data-literacy skills needed to analyze football effectively and to transition smoothly into more advanced analytical and modeling techniques.

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

6 videos Total 25 minutes

Understanding Central Tendency: Beyond the Basic Mean 3 minutes
Specialized Means: Weighted, Geometric, and Harmonic Means in Sports Analytics 4 minutes
The Normal Distribution: Understanding the Bell Curve and the Central Limit Theorem 5 minutes
Beyond the Bell Curve: Exponential, Gamma, and Poisson Distributions in Football Analytics 4 minutes
Understanding Variability: Standard Deviation as Your Analytical Compass 4 minutes
Confidence Intervals: Quantifying Uncertainty in Your Analysis 4 minutes

4 readings Total 175 minutes

Welcome reading 5 minutes
Complementary Resources on An Estimator for the Right Situation: Different kinds of means (average, median, mode, geometric, etc.). 75 minutes
Complementary Resources on Distributions: Normal, exponential, and gamma distributions 60 minutes
Complementary Resources on How to Know You Know: Standard deviations and confidence intervals 35 minutes

2 assignments Total 60 minutes

Making sense of the Numbers: Basic Mathematical Concepts 30 minutes
Making sense of the Numbers: Basic Mathematical Concepts 30 minutes

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

6 videos Total 27 minutes

Understanding Counting Data in Football Analytics 4 minutes
GPS and LPS Systems: Tracking Player Movement and Load 4 minutes
Understanding Event Data Structure and Provider Variations 5 minutes
From Data to Insights: Tactical Applications of Event Data 5 minutes
Understanding Tracking Data: From Broadcast to Multi-Angle Systems 5 minutes
The Rise of Skeleton Data: Mapping Players Beyond Position 4 minutes

3 readings Total 155 minutes

Complementary Resources on The First Ones: Counting & GPS data 40 minutes
Complementary Resources on A Big Step Forward: Event data and its variations depending on the provider 30 minutes
Complementary Resources on Increasing Context: Tracking (broadcast & multi-angle) and skeleton data 85 minutes

2 assignments Total 60 minutes

What Is Out There? Different Data Types 30 minutes
What Is Out There? Different Data Types 30 minutes

1 discussion prompt Total 10 minutes

What Is Out There? Different Data Types 10 minutes

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

6 videos Total 29 minutes

Understanding APIs: Gateways to Data in Modern Football Analytics 5 minutes
Web Scraping: Extracting Football Data from the Digital Landscape 5 minutes
Python for Football Data Processing: Transforming the Game with Code 5 minutes
Mastering Data Structures for Football Analytics: DataFrames, JSON, CSV, and XML 5 minutes
The Power of Data Visualization: Understanding the Fundamentals 4 minutes
Mastering Visualization Tools: Tableau, Power BI, and Matplotlib 5 minutes

3 readings Total 175 minutes

Complementary Resources on Gathering the Data: Interacting with APIs and web scraping 80 minutes
Complementary Resources on Processing: Introduction to Python, data structures (DataFrames, JSON, CSV, XML, etc.) 35 minutes
Complementary Resources on Visualizing: Tableau, Matplotlib (Python), Power BI 60 minutes

2 assignments Total 60 minutes

Working with Data: Coding and Software 30 minutes
Working with Data: Coding and Software 30 minutes

1 discussion prompt Total 10 minutes

Working with Data: Coding and Software 10 minutes

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

6 videos Total 24 minutes

The Two Faces of Data: Understanding Analytical vs. Rhetorical Approaches 5 minutes
The Power of Framing: How Presentation Shapes Data Interpretation 5 minutes
The Power of Simplicity: Why Our Brains Crave Clarity 3 minutes
From Data to Decisions: Creating Actionable Insights Through Simplification 3 minutes
The Psychology of Color in Data Visualization: From Perception to Persuasion 4 minutes
The Art of Scale: How Axis Choices Shape Data Perception 3 minutes

3 readings Total 145 minutes

Complementary Resources on What’s the Purpose? Analytical vs. rhetorical use of data 50 minutes
Complementary Resources on KISS (Keep It Simple, Stupid): Balancing detail with key, actionable takeaways 55 minutes
Complementary Resources on Chart Magic: How color choices and axis limits can guide (or mislead) attention 40 minutes

2 assignments Total 60 minutes

Communication 30 minutes
Communication 30 minutes

1 discussion prompt Total 10 minutes

Communication 10 minutes

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Instructor

Marisa Sáenz

Real Madrid Graduate School Universidad Europea

48 Courses 6,619 learners

Offered by

Real Madrid Graduate School Universidad Europea

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