Ergebnis für "A" in 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.

Welcome to "Predictive Power: Navigating the Landscape of AI Systems and Decision-Making." In this book, we embark on a journey into the fascinating world of artificial intelligence (AI) systems and their profound impact on decision-making processes across various domains. From business and healthcare to finance and beyond, AI technologies are revolutionizing and transforming how we analyze data, predict outcomes, and make informed choices.

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.  

Brought to you by Penguin. THE SUNDAY TIMES BESTSELLER AND A FINANCIAL TIMES, SUNDAY TIMES AND TELEGRAPH BOOK OF THE YEAR 2024 The insider account of the data revolution that has swept through the modern football world written by one of its key architects, Ian Graham. Between 2012 and 2023, Ian...

Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way statistical claims can be sensationalised, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of statistical literacy is more important than ever. In The Art of Statistics, David Spiegelhalter guides the listener through the essential principles we need in order to derive knowledge from data. 

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.

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...

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.

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. These new architectures have solid benefits, but they're also surrounded by a lot of hyperbole and confusion. This practical book provides a guided tour of these architectures to help data professionals understand the pros and cons of each.

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.

Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

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...

The greatest tool is the hardest to use. Machine learning is the world's most important general-purpose technology—but it's notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What's missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning initiatives, aka predictive AI projects, from conception to deployment.

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.

FinOps brings financial accountability to the variable spend model of cloud. Used by the majority of global enterprises, this management practice has grown from a fringe activity to the de facto discipline managing cloud spend. In this book, authors J. R. Storment and Mike Fuller outline the process of building a culture of cloud FinOps by drawing on real-world successes and failures of large-scale cloud spenders. Complete with a road map to get you started, this revised second edition includes new chapters that cover forecasting, sustainability, and connectivity to other frameworks.

This book consists of 6 titles, namely: Assembly Language: The Ultimate Guide to Programming and Virtual Machines. Computer Science: The Complete Guide to Principles and Informatics. Cyber Security: Beginners’ Guide to Hacking, Phishing, Social Engineering, and Malware. Databases: System Concepts, Designs, Management, and Implementation. Operating Systems: Concepts to Save Money, Time, and Frustration. Website Development: Web Design Skills for Beginners

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.

Brought to you by Penguin. We think we know bullshit when we hear it, but do we? A spotter's guide to bullshit in the wild from two brilliantly contrarian scientists The world is awash in bullshit, and we're drowning in it. Politicians are unconstrained by facts. Science is conducted by press...

“One of my favorite books of the year. It completely reshaped how I think about information and how and why I take notes.” —Daniel Pink, bestselling author of Drive A revolutionary approach to enhancing productivity, creating flow, and vastly increasing your ability to capture, remember...

Amazon Web Services is known to be a subsidiary under Amazon which has so far provided individuals, companies or even governments with platforms for on-demand cloud computing on a metered pay as you go basis. In total, these services about cloud computing provide some sort of primitive technical infrastructure that are technical and also distributed computing building blocks as well as tools.