Showing titles in Data Science

Ninety-four percent of consumers will leave a website if it has poor graphic design. Do you want to effortlessly keep an audience’s attention on your data visuals and not their phones? Keep listening.

In Generative AI: Navigating the Course to the Artificial General Intelligence Future, Martin Musiol delivers an incisive and one-of-a-kind discussion of the current capabilities, future potential, and inner workings of generative artificial intelligence. In the book, you'll explore the short but eventful history of generative artificial intelligence, what it's achieved so far, and how it's likely to evolve in the future. You'll also get a peek at how emerging technologies are converging to create exciting new possibilities in the GenAI space.

Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices.

In this book, you’ll find methods and techniques to organize your data, extract the information, and analyze it. There’s a way to make sure your company is going in the right direction, and this audiobook will reveal the secret behind a successful business.

Amazon Web Services in Action introduces you to computing, storing, and networking in the AWS cloud. The audiobook will teach you about the most important services on AWS. You will also learn about best practices regarding security, high availability, and scalability.

"Big Data" is on the covers of Science, Nature, The Economist, and Wired magazines, on the front pages of the Wall Street Journal and The New York Times. But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure.

One key responsibility of product designers and UX practitioners is to conduct formal and informal research to clarify design decisions and business needs. But there’s often mystery around product research, with the feeling that you need to be a research Zen master to gather anything useful. But anyone can conduct product research. With this quick reference guide, you’ll learn a common language and set of tools to help you carry out research in an informed and productive manner.

Structured query language, or SQL (pronounced "sequel" by many), is the most widely used programming language in database management and is the standard language for relational database management systems (RDBMS). SQL programming allows users to return, analyze, create, manage, and delete data within a database - all within a few commands.

In today's data-driven world, information is a powerful asset. But how do you ensure this information is reliable, secure, and usable? The CompTIA Data+ certification equips you with the foundational knowledge and skills to navigate the exciting realm of data storage, management, and analysis. This comprehensive guidebook serves as your roadmap to CompTIA Data+ mastery.

This book will open your eyes to the pitfalls of perception, and help you build a foundation for trust that elevates organizational performance... so you can make your data and AI initiatives succeed where others fail. Data, Strategy, Culture & Power is a must-listen for anyone who works with (or uses) data, analytics, and AI in today's complex organizations.

Structured Query Language (SQL) is the gold standard for interacting with modern databases. Used by companies all over the world, SQL allows users to manipulate the contents, architecture, and existence of a Relational Database Management System (RDBMS) with just a few commands. Knowledge of this simple, yet powerful tool is becoming ubiquitous in workplaces big and small, corporate and public. From young startups to multinational conglomerates - businesses of all types need to effectively manage and process their data.

Structured query language (SQL) is the gold standard for interacting with modern databases. Used by companies all over the world, SQL allows users to manipulate the contents, architecture, and existence of a relational database management system (RDBMS) with just a few commands. Knowledge of this simple yet powerful tool is becoming ubiquitous in workplaces big and small, corporate and public. From young start-ups to multinational conglomerates - businesses of all types need to effectively manage and process their data.

Blockchain is the technology behind Bitcoin, the revolutionary "virtual currency" that's changing the way people do business. While Bitcoin has enjoyed some well-deserved hype, Blockchain may be Bitcoin's most vital legacy. Blockchain for Dummies is the ideal starting place for business pros looking to gain a better understanding of what Blockchain is, how it can improve the integrity of their data, and how it can work to fundamentally change their business and enhance their data security.

When "metadata" became breaking news, appearing in stories about surveillance by the National Security Agency, many members of the public encountered this once-obscure term from information science for the first time. Should people be reassured that the NSA was "only" collecting metadata about phone calls - information about the caller, the recipient, the time, the duration, the location - and not recordings of the conversations themselves? Or does phone call metadata reveal more than it seems?

Ransomware attacks are no longer a question of if—it's a matter of when. With hackers increasingly targeting backup and disaster recovery (DR) systems, organizations need more than prevention strategies; they need a battle-tested plan for minimizing damage, forensically determining what's happened and restoring your environment without paying the ransom. Renowned experts W. Curtis Preston and Dr. Mike Saylor offer a comprehensive guide to protecting critical systems and responding effectively when the worst happens.

Radio 4's Book of the Week A Financial Times Book of the Year Shortlisted for the 2020 Financial Times / McKinsey Business Book of the Year Longlisted for the National Book Award 'The story of the original data science hucksters of the 1960s is hilarious, scathing and sobering - what you might...

The Inclusion Equation is a comprehensive, one-of-a-kind guide to merging DEI and employee wellbeing concepts with data analytics and AI. In this book, renowned thought leader and professional keynote speaker Dr. Serena H. Huang explains exactly how to quantify the effectiveness of new talent strategies by connecting them to a firm ROI estimate, enabling listeners to approach and win the favor of higher-ups in any organization with the same effectiveness that marketing and financial departments do.

This audiobook will help you get ready for the changes coming in the years ahead. Listen now. Packed with expert insights, the book addresses critical questions: How will AI alter employment, education, and global geopolitics? What ethical dilemmas will arise as humans interlace with AI through cyborgs and synthetic biology? Can we prepare for superintelligence and its utopian—or dystopian—outcomes?

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.

Between 1959 and 1989, Soviet scientists and officials made numerous attempts to network their nation - to construct a nationwide computer network. None of these attempts succeeded, and the enterprise had been abandoned by the time the Soviet Union fell apart. Meanwhile, ARPANET, the American precursor to the Internet, went online in 1969. Why did the Soviet network, with top-level scientists and patriotic incentives, fail while the American network succeeded? Find out.