Data Science Theory

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.

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee.

Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on “fixing” and “filling” academic gaps, we must envision and rebuild the system from the student up—with classrooms, schools and systems built around students’ brilliance, cultural wealth, and intellectual potential. Street data reminds us that what is measurable is not the same as what is valuable and that data can be humanizing, liberatory and healing.

From “one of the great (greatest?) contemporary popular writers on economics” (Tyler Cowen) comes a smart, lively, and encouraging rethinking of how to use statistics. Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The...

Gain a competitive edge in today’s data-driven world and build a rich career as a data professional that drives business success and innovation… Today, data is everywhere… and it has become the essential building block of this modern society. And that’s why now is the perfect time to pursue a career in data. But what does it take to become a competent data professional? This book is your ultimate guide to understanding the fundamentals of data analytics, helping you unlock the expertise of efficiently solving real-world data-related problems.

In this accessible and comprehensive audiobook, we start from scratch, assuming no prior knowledge. Each chapter is carefully crafted to guide you through the fundamental concepts and practical applications of AI, including machine learning, generative AI, and deep learning. With clear and plain English explanations, you'll be surprised at how quickly you can start thinking and speaking fluently in the words and mindset of an AI expert!

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...

Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data. With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap.

“An engaging voyage into some of the great mysteries and wonders of our world." --Alan Lightman, author of Einstein’s Dream and The Accidental Universe “No one is better at making the recondite accessible and exciting.” —Bill Bryson Brain Pickings and Kirkus Best Science Book of...

In Behavioral AI: Unleash Decision Making with Data, behavioral economist Dr. Rogayeh Tabrizi delivers an intuitive roadmap to help organizations disentangle the complexity of their data to create tangible and lasting value. The book explains how to balance the multiple disciplines that power AI and behavioral economics using a combination of the right questions and insightful problem solving.

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.

Welcome to the exciting world of AI-powered trading bots for executing options trades. In this subchapter, we will explore the fundamentals of AI and option trading, providing you with a solid foundation to begin your journey into the world of trading stocks and options. Whether you are a novice trader looking to learn the basics or an experienced investor seeking to leverage the power of AI technology in your trading strategies, this subchapter is designed to help you understand the key concepts and principles that drive success in the world of option trading.

Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them.

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.

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.

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.

This book will put AI, big data, the cloud, robotics, and smart devices in context. It will reveal how these technologies can dramatically multiply any businesses—including yours—by strategically using your data's latent, transformative potential.

This new edition introduces operation and design techniques for Sigma-Delta converters in physical and conceptual terms, and includes chapters which explore developments in the field over the last decade. Includes information on MASH architectures, digital-to-analog converter (DAC) mismatch and mismatch shaping

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.

Yes, you can succeed in an AI-driven world by unleashing your potential in machine learning! A working knowledge of AI and ML is no longer a “nice-to-have” but an “essential” in today's job market. But what if you feel lost amid the tech jargon? What if terms like “algorithms”, “neural networks”, or “data preprocessing” sound like a foreign language to you?