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ChatGPTと著者が二人三脚で執筆！「今の仕事にAIをどう使うか？　考え方一つで「AIの奴隷になるか、AIを奴隷にするか」くらいの大きな違いが出てくる」

ジブンの得意×AIを操るチカラ＝GPT時代の知的生産術ＣｈａｔＧＰＴ時代のＡＩは「ことばで動く」ので、文系人材でも自由自在に操ることができます。

「ChatGPT」（チャットジーピーティー）は⽂章⽣成AIです。⼈間が⼊⼒した質問に答える形で⽂章を返してくるので「対話型AI」とも呼ばれています。

技術書の表も裏も知り尽くした人気作家が、読書を血肉にするコツとテクニックを教えます。大好評の「技術書（コンピュータ書）」の読書術を指南する本のオーディオブック版が登場！

In this clear, practical, and beginner-friendly audiobook, bestselling author Maxen Ford demystifies the world of data and shows you how to build real skills that matter in today’s job market. Data Science for Non-Techies is designed for professionals, students, and career-changers who want to understand how data drives decisions—without needing to code or memorize complex formulas.

In an era where data drives every decision, mastering advanced data analytics is no longer optional—it’s essential. Mastering Advanced Data Analytics is your comprehensive guide to navigating the most current and powerful tools in analytics, including AutoML, reinforcement learning, and generative AI, equipping you with the skills to leverage data for maximum impact.

This audiobook is a summary of the advice I’ve given to software engineers over the years–and then some more. This audiobook follows the structure of a “typical” career path for a software engineer, from starting out as a fresh-faced software developer, through being a role model senior/lead, all the way to the staff/principle/distinguished level. It summarizes what I’ve learned as a developer and how I’ve approached coaching engineers at different stages of their careers.

Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it's often difficult to understand what each one offers. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.

Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently bingeable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.

In this comprehensive study guide, you will build a strong foundation in all topics covered on the certification exam, including the Databricks Lakehouse and its tools and benefits.

Data projects are an intrinsic part of an organization's technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively. Data mesh is one solution that provides a new approach to managing data in complex organizations. This practical guide offers step-by-step guidance on how to implement data mesh in your organization.

Celebrated data-driven growth leader Lillian Pierson delivers a masterclass in developing custom strategies to harness the full potential of data and AI within your organization. This book offers a clear, actionable roadmap for leveraging your company's data and technology assets to drive significant, reliable growth.

Foreword by Steven Pinker Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating, illuminating, and witty look at what the vast amounts of information now instantly available to us reveals about ourselves and our...

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, this book presents ninety-seven concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data.

A potent exploration of the power of blockchains to reshape the future of the internet—and how that affects us all—from influential technology entrepreneur and startup investor Chris Dixon “A must for anyone who wants to better understand the real potential of blockchains and web3...

In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very concept of the firm. AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value.