Data Science for Dummies
2nd Edition
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Audible会員プラン 無料体験
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ナレーター:
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Chloe Cannon
このコンテンツについて
Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. 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.
While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here's what to expect:
- Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value.
- Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL.
- And more...
It's a big, big data world out there - let Data Science for Dummies help you harness its power and gain a competitive edge for your organization.
©2017 John Wiley & Sons, Inc. (P)2019 Tantorこちらもおすすめ
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In this audiobook, you'll get a clear overview of artificial intelligence technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field.
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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.
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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.
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クリエイティブになるには、数学力を高めるのが早道だ!AIが進化した今日、人間に求められるのは考える力や創造力であり
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Need networking know-how, but don't know where to turn? Run-don't walk-to the no-nonsense networking guidance offered in this friendly guide! Whether you're a networking administrator or an everyday computer user looking to set up a network in your home or office, Networking for Dummies, seamlessly gets you connected with the basics and gives you the knowledge to work out whatever kinks may come your way-in no time.
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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.
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Deep Learning for Finance
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Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning.
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Learning Systems Thinking
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A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.
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Fundamentals of Data Engineering
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Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.
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Game Theory
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Games are everywhere: Drivers maneuvering in heavy traffic are playing a driving game. Bargain hunters bidding on eBay are playing an auctioning game. The supermarket's price for corn flakes is decided by playing an economic game. This Very Short Introduction offers a succinct tour of the fascinating world of game theory, a groundbreaking field that analyzes how to play games in a rational way.
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