Dimensionality Reduction and Visualization with Plotly

High-dimensional datasets are incredibly common in modern data analysis, but they are notoriously difficult to interpret and visualize. This text-based course provides a clear, step-by-step path to understanding how to compress complex data while preserving its most critical information. You will start with the fundamental mathematics and concepts behind dimensionality reduction, ensuring you have a strong theoretical foundation before writing any code. By working through practical written explanations and code examples, you will learn how to prepare your data, apply principal component analysis, and construct interactive visualizations that bring hidden patterns to light. We also cover modern data analysis practices, including proper feature scaling pipelines and handling high-dimensional noise. What you'll learn: - Understand the core concepts of high-dimensional data and the math behind dimensionality reduction - Apply Principal Component Analysis (PCA) to reduce dataset features while retaining maximum variance - Construct interactive 2D and 3D scatter plots using Plotly Express to explore reduced data - Implement proper data preprocessing and feature scaling pipelines before applying reduction techniques - Interpret explained variance ratios to determine the optimal number of principal components - Analyze and visualize real-world datasets to uncover hidden clusters and patterns This course begins with essential definitions of vector spaces and dimensions, then guides you through data preparation, model execution, and interactive plotting techniques. It is designed for beginners, data analysts, and aspiring data scientists who want to master dimensionality reduction without needing prior experience in advanced statistics. Dive into the text and start clarifying your high-dimensional data today.