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.

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.

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.

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.

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.

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...

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.

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 ...

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.

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.

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.

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.

Whether you're new to the world of programming and are looking for clarity on what direction to take, or you're simply curious about what machine learning, AI, and big data has in store for the future and want to prepare for it, this guide makes it easy to actually understand implement machine learning and data analytics effectively.

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 ...

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.

There are two remarkable phenomena that are unfolding almost simultaneously. The first is the emergence of a data-first world, where data has become a central driving force, shaping industries and fueling innovation. The second is the dawn of the AI age, propelled by the advent of Generative AI, that has created the possibility to leverage the data of the world for the first time. The convergence of these two, with data as the common denominator, holds immense promise and the opportunities are boundless.

Unlock the power of data and artificial intelligence with the Fundamentals Series—a comprehensive two-book collection designed to equip you with the essential skills to succeed in today’s technology-driven world. Whether you’re looking to build a career in data analytics, harness the potential of AI, or monetize cutting-edge tools like large language models, this series provides the ultimate roadmap.

In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally - hiring, driving, paying bills, even choosing romantic partners - that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology.

A team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings.