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Discover Patterns in MMA with Python, Pandas, and Statistics

Learn how to analyze real-world data using beginner-friendly Python tools and techniques. You’ll work with Pandas to clean and manipulate datasets, use NumPy for efficient numerical computations, and create compelling visualizations with Matplotlib to bring your findings to life. Along the way, you’ll apply fundamental statistical methods to uncover hidden patterns, test meaningful hypotheses, and translate raw numbers into actionable insights. By the end of the project, strengthen your coding and analysis skills and walk away with a practical understanding of how to analyze sports data.

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Guided Project

Data Science

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At a Glance

Learn how to analyze real-world data using beginner-friendly Python tools and techniques. You’ll work with Pandas to clean and manipulate datasets, use NumPy for efficient numerical computations, and create compelling visualizations with Matplotlib to bring your findings to life. Along the way, you’ll apply fundamental statistical methods to uncover hidden patterns, test meaningful hypotheses, and translate raw numbers into actionable insights. By the end of the project, strengthen your coding and analysis skills and walk away with a practical understanding of how to analyze sports data.

Mixed Martial Arts (MMA) is one of the fastest-growing sports in the world, but what really gives fighters an edge inside the octagon? Many fans and analysts believe that a fighter’s reach plays a critical role in determining fight outcomes—but is this backed by data?
In this project, you’ll dive into real UFC fight data to answer this question. Along the way, you’ll practice data cleaning, feature engineering, visualization, and statistical testing, all essential skills for any aspiring data analyst or data scientist. By the end, you’ll have the ability to not only tell a compelling sports story but also back it up with evidence.

Here’s what you’ll learn (and be able to do) by completing this project:
  • Learning Objective 1: Clean and preprocess real-world sports data, engineer new features, and filter the dataset to focus on meaningful comparisons.
  • Learning Objective 2: Apply visualization and statistical analysis (including two-proportion z-tests and CUSUM charts) to uncover whether a longer reach actually improves a fighter’s chance of winning.
By the end, you’ll walk away with a mini data science case study you can showcase—proof that you can take a raw dataset, ask the right questions, and communicate insights with both visuals and statistics.

What You'll Need

To complete this project, you’ll need some basic Python programming knowledge, familiarity with pandas and matplotlib, and a beginner-level understanding of statistics (such as means, proportions, and significance testing). The IBM Skills Network Labs environment comes with all the required tools pre-installed, so you won’t have to worry about setup or configuration. Simply log in and start working through the guided steps. For the best experience, we recommend using a modern browser such as Chrome, Edge, Firefox, Internet Explorer, or Safari.

Estimated Effort

30 Minutes

Level

Beginner

Skills You Will Learn

Data Visualization, General Statistics, Matplotlib, Numpy, Pandas, Python

Language

English

Course Code

GPXX01KREN

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