Showing titles in Data Science

Go in Action is for any intermediate-level developer who has experience with other programming languages and wants a jump-start in learning Go or a more thorough understanding of the language and its internals. This book provides an intensive, comprehensive, and idiomatic view of Go. It focuses on the specification and implementation of the language, including topics like language syntax, Go's type system, concurrency, channels, and testing.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.  

Big data knows where you've been and who your friends are. It knows what you like and what makes you angry. It can predict what you'll buy, where you'll be the victim of crime and when you'll have a heart attack. Big data knows you better than you know yourself, or so it claims. But how well do you know big data? You've probably seen the phrase in newspaper headlines, at work in a marketing meeting, or on a fitness-tracking gadget. But can you understand it without being a Silicon Valley nerd who writes computer programs for fun? Yes. Yes, you can.

If you are a budding analyst or want to freshen up on your concepts, this book is for you. It has all the basic information you need to help you become a data analyst or scientist.

This audiobook has lots of valuable, eye-opening information about data analytics, which will help you understand the concept of data mining, data collection, big data analytics for business, and business intelligence concepts. With this audiobook, not only will you understand all the internal nitty-gritties about data analytics, you will also understand why data analytics is changing the business arena. You’ll realize that the high-performance analytics will enable you to do stuff that you never thought about before.

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?

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.

R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing.   

Law today is incomplete, inaccessible, unclear, underdeveloped, and often perplexing to those whom it affects. In The Legal Singularity, Abdi Aidid and Benjamin Alarie argue that the proliferation of artificial intelligence-enabled technology–and specifically the advent of legal prediction–is on the verge of radically reconfiguring the law, our institutions, and our society for the better.

Using data to guide decision making builds stronger, more adaptable organisations that can thrive in a rapidly changing world. Embedding a pragmatic, data-guided culture in any organisation can be a challenge. This comprehensive guide for leaders sets out a proven framework for developing the mindset and strategies required to generate value from data and to scale quickly. Jargon-free and packed with practical advice, Data Means Business can help you activate your data to generate growth.

List the hottest buzzwords in business today and "analytics" is bound to come out near the top. But to most people, analytics implies complex tools and technical experts manipulating massive amounts and varieties of information (i.e.,"Big Data"). But complex Big Data analytics isn't the only game in town. And many people don't realize that a business doesn't need data scientists and complex tools to derive useful information and valuable benefits from any amount of raw data.

Computer science is a field that deals with the study of computers and computational systems. It encompasses both the theoretical and practical aspects of computing, including algorithms, data structures, programming languages, software engineering, and more. It's about understanding how computers work, how they can solve problems, and how they interact with humans and other systems.

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Data Lake Architecture will explain how to build a useful data lake where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess.

Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data.

In this book, Michael Smith and Rahul Telang, experts on entertainment analytics, show how the success of House of Cards upended the film and TV industries - and how companies like Amazon and Apple are changing the rules in other entertainment industries, notably publishing and music. We're living through a period of unprecedented technological disruption in the entertainment industries.

The best-selling author of Big Data is back, this time with a unique and in-depth insight into how specific companies use big data. Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective.

This audiobook teaches you how to build an intelligent system from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems.  

The Microsoft Access Database comes with its own programming language known as Visual Basic Applications. It provides a powerful way that allows automation and usually controls how an access database application operates. Even though it is easy to use, getting started can tend to be overwhelming or intimidating for beginners. To make things easy for you to learn and understand we have come up with this powerful yet short book. This incredible product is intended to help you feel at home in a programming environment while learning access VBA programming quickly.

Scott Hartley first heard the terms fuzzy and techie while studying political science at Stanford University. If you majored in the humanities or social sciences, you were a fuzzy. If you majored in the computer sciences, you were a techie. This informal division has quietly found its way into a default assumption that has misled the business world for decades: that it's the techies who drive innovation.