Essential Calculus and Numerical Analysis for Data Science

Data science is built on a foundation of mathematics, yet the transition from basic arithmetic to complex algorithms can feel overwhelming. This course bridges that gap by providing a clear, text-based exploration of the calculus and numerical techniques that power modern data analysis. You will transform your understanding of how data models function under the hood, moving from simple calculations to grasping the mechanics of optimization and statistical modeling. Through detailed written explanations and logical exercises, you will learn to navigate the mathematical landscape of data science with confidence. What you'll learn: - Understand the fundamental concepts of integral calculus and area under a curve - Apply integration by parts and numerical integration to solve data-related problems - Master root-finding methods and matrix decomposition for computational efficiency - Practice calculating partial derivatives for multi-variable optimization - Explore modern gradient-based optimization and the basics of automatic differentiation - Analyze numerical stability and error propagation in algorithmic workflows The course starts with essential terminology and foundational definitions, ensuring you have a solid conceptual base before progressing to more complex numerical methods and calculus applications in data workflows. This course is designed for beginners and aspiring data professionals who want to build a strong mathematical background from scratch. No prior experience with advanced calculus is required. Begin your journey toward mathematical literacy in data science today.