Data Science: The Hard Parts Subtitle: Techniques for Excelling at Data Science Author: Daniel Vaughan

Data Science: The Hard Parts by Daniel Vaughan is an advanced, no-nonsense guide for those ready to tackle the complexities that most data science books gloss over. Designed for intermediate to seasoned practitioners, this book cuts to the chase, focusing on the gritty, high-impact problems encountered in real-life projects—from interpreting messy data, managing model drift, navigating ethical dilemmas, and communicating effectively with stakeholders.

Vaughan blends deep technical acumen with practical case studies to illustrate how theoretical knowledge can be transformed into scalable solutions. He dives into essential skills that separate good data scientists from great ones—like debugging models, designing robust experiments, and balancing business value with technical rigor. Each chapter offers distilled , decision frameworks, and hard-earned strategies honed through years of experience.

Whether you’re refining your machine learning workflows or grappling with ambiguous business questions, this book equips you with the clarity, confidence, and tactical know-how to lead data-driven initiatives that make a measurable difference. It’s a must-have for professionals looking to sharpen their edge in one of today’s most dynamic fields.