Numerical Foundations for Data Science

Many aspiring data professionals struggle not with writing code, but with understanding the underlying mathematics that powers modern algorithms. This text-based course bridges that gap by breaking down complex mathematical concepts into clear, digestible explanations. You will transition from memorizing formulas to truly understanding how data models work under the hood. By working through structured readings and written exercises, you will develop the numerical intuition needed to analyze datasets confidently, choose the right models, and explain your technical decisions during interviews. What you'll learn: - Understand foundational linear algebra concepts including vectors, matrices, and eigenvalues - Apply core calculus principles like derivatives and gradients to optimization problems - Master probability theory, Bayes' theorem, and probability distributions for predictive modeling - Implement statistical hypothesis testing and confidence intervals to validate data assumptions - Explore modern data concepts like vector embeddings and dimensionality reduction techniques - Practice solving typical numerical interview questions using step-by-step written breakdowns The course begins with fundamental terminology and core mathematical concepts before guiding you through practical applications in statistics, probability, and optimization. This logical progression ensures you build a reliable framework for analyzing data. This course is designed for beginners, aspiring data analysts, and junior data scientists who want to build a strong mathematical foundation without needing prior advanced math experience. Start building your numerical capabilities and master the mathematics of data science today.