Data Science Theory

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

The Logic of Communication: Exploring the Principles and Applications of Information Theory takes you inside one of the most important scientific revolutions of the modern age. From Claude Shannon’s groundbreaking insights in the 1940s to the ways information theory powers today’s digital world, this book reveals how the hidden rules of communication shape everything we do. Discover how information can be measured, compressed, secured, and transmitted across noisy channels with remarkable accuracy. Learn why entropy is central to understanding uncertainty, how redundancy and error ...

Data Science for Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, listening to this book will help you understand what technologies, programming languages, and mathematical methods on which to focus.

In the age of artificial intelligence, machine learning, and data-driven innovation, the quality and quantity of data have become critical to success. Yet real-world data is often incomplete, biased, sensitive, or scarce. Data Generation explores how synthetic, real, and simulated data can be strategically created and used to overcome these limitations—fueling smarter systems, scalable solutions, and more ethical outcomes.

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX! Deep Learning with Python (Third Edition) puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.

James Gleick, the author of the best sellers Chaos and Genius, now brings us a work just as astonishing and masterly: a revelatory chronicle and meditation that shows how information has become the modern era’s defining quality—the blood, the fuel, the vital principle of our world. The...

An all-encompassing guide to skeptical thinking from podcast host and academic neurologist at Yale University School of Medicine Steven Novella and his SGU co-hosts, which Richard Wiseman calls "the perfect primer for anyone who wants to separate fact from fiction." It is intimidating to realize...

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

In our highly competitive world of today, we need all the edge we can to get ahead of our competitors. For a long time, companies have focused on spending more on marketing and advertisement, ignoring the powerful asset they had at their disposal: data. Although this isn't the case anymore for large companies, smaller businesses are still in the dark about this, and you can use this asset to your advantage. This bundle will show you how.

Expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data-centric issues. Discover two comprehensive manuscripts in one audiobook.

Factories create massive amounts of data from IoT sensors, MES, SCADA, quality systems, supply chain, maintenance, and others. Yet most organizations can’t turn it into decisions fast enough. The result is reactive firefighting and AI pilots that stall. The Unified Manufacturing Data Architecture (UMDA) is a practical framework built for industry, not adapted from IT. It shows you how to handle real-time streams, integrate legacy systems, enforce security, and scale AI across sites.

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars.

From Data to Decisions reveals how AI turns the chaos of modern information into clarity, intelligence, and power. Through ten illuminating chapters, Julian Vexley explores the mechanisms, logic, and creativity behind the most important technological revolution of our time—the rise of systems that can learn, predict, and decide.

It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets.

The deep-learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep-learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Dive into the mysteries of the cosmos with Simulation vs Singularity, a captivating exploration of the universe's most mind-bending theories. This eBook takes readers on an exhilarating journey through the intricacies of cosmic models, uncovering the secrets of black holes and the tantalizing notion that our reality might just be an elaborate simulation. Start with an introduction to cosmology, where theoretical concepts transform into the building blocks of our understanding. Unravel the enigmatic nature of black holes, from the formidable singularities and event horizons to the ...

The ultimate guide of how-to templates with real-world examples that demystify the art and science of AI prompt writing, offering clear frameworks, proven templates, and step-by-step instructions. This empowering guide reveals the exact formulas behind successful prompts with structured templates, troubleshooting guides, and practical exercises. Develop the critical thinking skills needed to achieve consistent, high-quality AI outputs.

Un científico de datos es alguien que sabe más de informática que un estadístico, y conoce más estadísticas que un científico de la computación. Si bien, en gran medida, esto es cierto, convertirse en un buen científico de datos requiere el dominio no solo de estas dos áreas clave, sino también de algunas teorías y modelos cruciales para este campo. El libro se recomienda a todos los lectores interesados ​​que aspiran a destacarse en el campo de la ciencia de datos.

In Fusion Strategy, world-renowned innovation guru Vijay Govindarajan and digital strategy expert Venkat Venkataraman offer a first-of-its-kind playbook that will help industrial companies combine what they do best-create physical products-with what digitals do best-use algorithms and AI to parse expansive, interconnected datasets-to make strategic connections that would otherwise be impossible.

Today, 91 percent of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence. So, future programmers will no longer find work without having knowledge of this particular field that is constantly evolving.