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Informationen zum Autor Field Cady is a data scientist, researcher and author based in Seattle, WA, USA. He has worked for a range of companies including Google, the Allen Institute for Artificial Intelligence, and several startups. He received a BS in physics and math from Stanford and did graduate work computer science at Carnegie Mellon. He is the author of The Data Science Handbook (Wiley 2017). Klappentext 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. Inhaltsverzeichnis Preface to the First Edition xvii Preface to the Second Edition xix 1 Introduction 1 1.1 What Data Science Is and Isn't 2 1.2 This Book's Slogan: Simple Models Are Easier to Work With 3 1.3 How Is This Book Organized? 4 1.4 How to Use This Book? 4 1.5 Why Is It All in Python, Anyway? 4 1.6 Example Code and Datasets 5 1.7 Parting Words 5 Part I The Stuff You'll Always Use 7 2 The Data Science Road Map 9 2.1 Frame the Problem 10 2.2 Understand the Data: Basic Questions 11 2.3 Understand the Data: Data Wrangling 12 2.4 Understand the Data: Exploratory Analysis 12 2.5 Extract Features 13 2.6 Model 14 2.7 Present Results 14 2.8 Deploy Code 14 2.9 Iterating 15 2.10 Glossary 15 3 Programming Languages 17 3.1 Why Use a Programming Language? What Are the Other Options? 17 3.2 A Survey of Programming Languages for Data Science 18 3.3 Where to Write Code 20 3.4 Python Overview and Example Scripts 21 3.5 Python Data Types 25 3.6 GOTCHA: Hashable and Unhashable Types 30 3.7 Functions and Control Structures 31 3.8 Other Parts of Python 33 3.9 Python's Technical Libraries 35 3.10 Other Python Resources 39 3.11 Further Reading 39 3.12 Glossary 40 3a Interlude: My Personal Toolkit 41 <...
