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The Data Science Handbook
出版済み 3-5週間でお届けいたします。
データサイエンスハンドブック 第2版 Author: Cady, Field (Allen Institute for Artificial Intelligence; Stanford University; Carnegie Mellon) Publisher: Wiley ISBN: 9781394234493 Cover: HARDCOVER Date: 2024年10月 こちらの商品は学校・法人様向け（機関契約）のオンラインブック版がございます。 オンラインブックの価格、納期につきましては弊社営業員または当ECサイトよりお問い合わせください。  DESCRIPTION データサイエンティストの仕事は数学的なツールだけでなく、ソフトウェアエンジニアリングやビジネスの理解、データそのものへの深い知識も求められます。本書では必要なスキルを統合し、理論に偏らず実際の問題解決に役立つ実践的なデータサイエンスの応用事例を解説します。 Practical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice. Among its key points are: * An emphasis on software engineering and coding skills, which play a significant role in most real data science problems. * Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, and programming paradigms). * A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more. * Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization. * Exactly the right amount of theory. A solid conceptual foundation is required for fitting the right model to a business problem, understanding a tool’s limitations, and reasoning about discoveries. Data science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. Much of data science has become a skillset that anybody can have, making this book not only for aspiring data scientists, but also for professionals in other fields who want to use analytics as a force multiplier in their organization. TABLE OF CONTENTS 1 Introduction Part I The Stuff You’ll Always Use 2 The Data Science Road Map 3 Programming Languages 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning 5 Visualizations and Simple Metrics 6 Overview: Machine Learning and Artificial Intelligence 7 Interlude: Feature Extraction Ideas 8 Machine-Learning Classification 9 Technical Communication and Documentation Part II Stuff You Still Need to Know 10 Unsupervised Learning: Clustering and Dimensionality Reduction 11 Regression 12 Data Encodings and File Formats 13 Big Data 14 Databases 15 Software Engineering Best Practices 16 Traditional Natural Language Processing 17 Time Series Analysis 18 Probability 19 Statistics 20 Programming Language Concepts 21 Performance and Computer Memory Part III Specialized or Advanced Topics 23 Maximum-Likelihood Estimation and Optimization 24 Deep Learning and AI 25 Stochastic Modeling 26 Parting Words: Your Future as a Data Scientist
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