Data Science

Most analytics books teach tools first and thinking later. Data Analytics Foundations reverses that order — building the judgment, technical skills, and communication discipline required to transform raw data into trusted decisions. From spreadsheets and SQL to data quality, visualization, and analytical reasoning, this book equips readers with a complete foundation for professional analytical practice.

Most data modelling books teach you the craft. This one teaches you the reality. Seven chapters of honest, practical guidance from real project experience — covering conceptual modelling, governance, enterprise challenges, and the human side of data work that nobody else writes about.

In a world of infinite AI-generated text, fluency is no longer a credential—trust is. Learn how to engineer "signal" into your technical writing and build an authoritative presence that algorithms can't replicate. This is the definitive practitioner’s guide for publishing in the AI era.

You are not bad at data. Your work has outgrown the shape of Excel. This book helps finance, HR, operations, reporting, and business analysis professionals replace manual spreadsheet survival with calmer, reusable workflows in R.

A hands-on guide to building, scheduling, and deploying data pipelines with Apache Airflow 2.x from scratch to AWS MWAA production deployment.

If machines are ever going to think, they have to do more than process data — they have to understand it.Integrating Data Fusion and Cognitive Architectures: Volume I – Foundations and Mechanisms is the blueprint for that transformation. It bridges two worlds that have lived apart for decades: the hard mathematics of sensor fusion and the structured reasoning of cognitive science. The result? A unified system that can perceive, reason, and adapt — not someday, but now.

Introducing Data Analytics and Data Processing Essentials: Unlocking Visualization, SQL, and AI Techniques for Modern Data Science—your comprehensive guide to navigating the fast-paced world of data analytics. Whether you're just starting or looking to elevate your skills, this book provides the essential knowledge and practical tools you need to thrive in today’s data-driven industries.

Master the art of AI interaction with 22 proven prompting techniques, real code examples, and production-tested strategies. From the creator of GitHub's most-starred prompt engineering repository (7,100+ stars).

AI engines are booming, and the more we work with agentic systems, the more we see that we need something to make them work at the enterprise level. We're quite active in exploring ideas around context graphs, decision traces, and supporting explainability—giving agents the ability to make more aware and company-aligned decisions.But this makes sense not only for enterprises, but for users and individuals building personal agents as well. Unfortunately, we have zero-to-none inclination on how to actually build a context graph.I'll try to explain how to build something like a context graph—but go beyond it. I deeply believe that to make this work, we need specific agentic memory and a set of cognitive processes that truly help agents use this memory and learn from experience and data.That's why this is the Book: Beyond Context Graphs—with a focus on real-life enterprise tasks and how to make agents make better decisions and, let's say, hallucinate less.

Most people think they are bad at math. In reality, they were never taught arithmetic properly.This book is a modern English edition of Arithmetic by Alexander P. Kiselev—the text that formed the backbone of mathematical education in Russia and USSR for over a century and helped produce generations of exceptionally strong mathematicians, scientists, and engineers.Unlike modern textbooks that prioritise shortcuts, visuals, and lowered expectations, Kiselev builds arithmetic logically, systematically, and rigorously. Every method is explained. Every operation has meaning. Exercises are carefully sequenced to develop real understanding—not rote pattern-following.This book does not promise “easy math”. It promises something better: clarity, confidence, and competence.Whether you are a student, a parent, a tutor, or an adult rebuilding fundamentals, this book will change how you understand arithmetic—and why so much later mathematics suddenly becomes easier.

Bridge the gap between HYSYS simulations and industrial reality using Physics-Informed AI and MATLAB

Machine learning doesn’t fail in theory—it fails in production. This book shows you how to build PyTorch systems that remain robust when data shifts, assumptions break, and reliability matters.

What could a mighty billion-parameter reasoning machine learn from a camel trying to touch its ear with its tongue? From a cup of coffee? From deleting your entire codebase while you sleep? More than you’d think. And less than you’d hope. Today’s AI is brilliant structure without grounding—a hollow genius. We chase smarter models but ignore the architecture they need. This book is about building that missing layer: the trust chains and systems that turn raw intelligence into reliable autonomy. For builders ready to move beyond prompts.Watch agents solve unsolvable problems. Learn to think in trust chains. Start here.

Stop Scripting. Start Discovering. 🚀 You’ve mastered Python’s syntax—now it’s time to master the data. Volume 10 marks your transition from imperative coding to the vectorized, analytical mindset of a Data Scientist. Move beyond "copy-paste" snippets and learn the architectural foundations of NumPy, Pandas, and Scikit-Learn. Don’t just write code; derive intelligence.