Data Science

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.

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.

A practical field guide for using AI assistants at work—and engineering them in production. Learn the standards that prevent “sounds right, wrong” outputs: specs, grounding, tools, evals, guardrails, and cost control.

What if peace has never failed—only our systems have? For thousands of years, humanity has tried to govern itself through kings, empires, ideologies, and democracies—yet conflict persists. Homocracy and Digital Public Infrastructure exposes the hidden flaw shared by all traditional systems: the violation of individual free will. This book introduces Homocracy, a new model of governance where every human being is an equal shareholder, leadership is a service, and power cannot be centralized. Grounded in The Absolute Truth of Existence (TAToE) and enabled by modern Digital Public Infrastructure, Homocracy transforms governance from coercion into cooperation. Not a political manifesto. Not a utopia. A blueprint for a peaceful human civilization—engineered, not imagined.

Mastering Advanced Time Series Forecasting in Python is the definitive sequel to the #1 forecasting bestseller. Designed for practitioners who want to go beyond ARIMA and basic ML, this book takes you deep into probabilistic forecasting, hierarchical coherence, and cutting-edge foundation models—backed by production-ready Python code. Learn how to assess forecastability, build scalable pipelines, quantify uncertainty, and deploy systems that deliver real business impact. Written by a globally recognized expert whose methods power multimillion-dollar decisions, this is the practical, honest, and advanced guide every data scientist, ML engineer, and quantitative professional needs to master modern forecasting.

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.

Are you a data scientist or analyst struggling to take your Jupyter Notebook prototypes to the next level? Have you encountered challenges with code organization, reproducibility, or collaboration as your data science projects grow in complexity? This book is the solution you’ve been seeking. This comprehensive guide bridges the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and collaborative solutions.

Discover how modern web scraping goes beyond simple scripts. Learn to extract, automate, and transform data from dynamic websites using Python, Playwright, undetected-chromedriver, Chrome DevTools MCP, and AI tools. Packed with hands-on examples and expert tips, this book shows you how to build ethical, scalable, and intelligent scraping workflows that turn raw web content into actionable insights.

A model that wins on MAPE can lose money on the shelf. A 15% accuracy gain can hide a 12% bias. An interval that looks tight can be wrong a third of the time.Forecast metrics are decision instruments, not neutral truth detectors — and most teams are still choosing them by habit. This book shows where MAE, MAPE, sMAPE, WAPE, MASE, RMSSE, CRPS, pinball loss, energy scores, bias measures and cost-based metrics help, and where they quietly mislead.Twelve chapters take you from elicitation and baselines through intermittent demand, probabilistic and hierarchical scoring, and the accuracy–utility gap, to a production evaluation system with monitoring, drift detection, alerting and governance. Every metric arrives with its assumptions, every criticism carries a remedy, and a retrospective Walmart M5 case study runs throughout, with Python listings and reproducibility checks.For demand planners, forecasting data scientists, ML engineers and analytics leads who need evaluation choices they can defend. Measure what matters.

This book reveals the essential mathematics powering AI, data science, and finance, making complex concepts accessible and exciting for every reader. Discover the practical applications of math that are already influencing your daily life.

Turn uncertainty into a competitive advantage with probabilistic forecasting and Conformal Prediction.