Applied Calculus for Data Science

Reactive Publishing

In the data-driven world of 2025, knowing how to code isn't enough. To truly understand and build powerful models, you need to master the math that powers them-starting with calculus.

Applied Calculus for Data Science is your practical guide to understanding the real-world application of calculus in modern data workflows. Whether you're training machine learning models, optimizing loss functions, or interpreting trends in big data, this book breaks down the core calculus concepts that every data scientist needs-without the fluff.

Inside, you'll explore:

Derivatives & Gradients - the backbone of optimization algorithms

Integrals & Area Under the Curve - from probability to AUC-ROC curves

Multivariable Calculus - powering neural networks, backpropagation, and more

Hands-on examples with Python - bringing theory to life with code

Use cases in machine learning, statistics, and deep learning

Designed for accessibility without sacrificing depth, this book is ideal for students, self-taught developers, analysts, and anyone preparing for a career in AI, data science, or fintech.

Understand the math. Build smarter models. Take control of your algorithms.