Use basketball stats to optimize game play with Visual Studio Code, inspired by SPACE JAM: A NEW LEGACY

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Module 1: Use data from basketball players in the film *Space Jam: A New Legacy* to create a machine learning model to cleanse data and explore bimodal data distributions.

In this module, you will:

Tip

This module is part of a multimodal learning experience. Start the module to see how you can follow along!

Module 2: Create a web app by using JavaScript, Azure, GitHub, and Visual Studio Code to help the Tune Squad coach use data to make decisions on the basketball court, inspired by SPACE JAM: A NEW LEGACY.

In this module, you will begin to discover:

Tip

This module is part of a multimodal learning experience. Start the module to see how you can follow along!

Module 3: Discover the fundamentals of Mixed Reality, and prepare the resources you need to create an interactive experience for basketball fans and coaches inspired by *Space Jam: A New Legacy*.

In this module, you'll:

Discover the world of Mixed Reality.
Explore ways to integrate data into Mixed Reality experiences.
Design a Mixed Reality experience to display basketball player data to fans and coaches.

Module 4: Learn the core concepts of building Mixed Reality experiences with Babylon.js. We'll create a simple WebXR app that can be run cross-platform.

In this module, you will:

Discover the world of Mixed Reality with Babylon.js.
Create a Mixed Reality experience to display data about basketball players to fans and coaches.

Module 5: Use C#, Visual Studio, Unity, and the Mixed Reality Toolkit to create an interactive virtual reality experience compatible with the HoloLens. Inspired by *Space Jam: A New Legacy*, you create an experience for coaches and fans to learn more about the Tune Squad.

In this module, you will:

Module 1: Predict basketball player efficiency ratings by using machine learning and Visual Studio Code

Introduction
Exercise - Set up your local environment for data science coding
Exercise - Data cleansing part 1 - Find missing values
Exercise - Data cleansing part 2 - Drop columns and rows
Exercise - Data exploration part 1 - Check for outliers
Exercise - Data exploration part 2 - Check the distribution of the data
Exercise - Data exploration part 3 - Discover data that represents more than one population
Exercise - Data manipulation part 1 - Add qualifying player information
Exercise - Data manipulation part 2 - Impute missing values for columns
Exercise - Data manipulation part 3 - Impute missing values by using machine learning
Knowledge check
Summary

Module 2: Create a web app that uses data to make decisions on the basketball court

Introduction
Exercise - Set up your local environment for data science coding
Exercise - Prepare data by adding row qualifiers
Exercise - Create a machine learning model based on player data
Exercise - Use machine learning and standard deviation to create fictional game data
Exercise - Create a starter web app
Exercise - Use CodeTour to build your web app functionality
Exercise - Write JavaScript code to initialize the web app with data from the machine learning model
Exercise - Add players to the bench in your web app with JavaScript
Exercise - Add PER stats to the web app
Exercise - Add button functionality for player buttons
Exercise - Complete the stat-updating functionality
Exercise - Complete the SPACE JAM: A NEW LEGACY Water Break App
Check your knowledge
Summary