Distance Metrics and Vector Functions for Data Science

Measuring distance and similarity between data points is a fundamental requirement in data analysis, machine learning, and algorithm design. This written course guides you through the formal mathematical definitions of metrics and demonstrates how to implement distance functions programmatically. You will start by learning core definitions and foundational concepts—including non-negativity, symmetry, and the triangle inequality—before exploring standard distance functions such as Euclidean, Manhattan, Minkowski, Cosine, and Hamming metrics. What you'll learn: Understand the formal mathematical axioms that define a valid metric space; Explore standard distance functions including Euclidean, Manhattan, Minkowski, and Cosine metrics; Calculate distance values accurately using Python code snippets and numerical libraries; Apply distance metrics to common tasks in clustering, classification, and data processing; Evaluate modern vector similarity measures used in text embeddings and search algorithms. You will progress from essential definitions to practical code implementations and written practice exercises designed to solidify your theoretical and applied understanding. This course is intended for beginner data analysts, programmers, and students seeking a clear foundation in metric spaces without needing advanced prior experience. Start reading today to build a strong practical understanding of distance calculations.