About this Book Data science still carries the aura of a new field. Most of its components—statistics, software development, evidence-based problem solving, and so on—descend directly from well-established, even old, fields, but data science seems to be a fresh assemblage of these pieces into something that is new, or at least feels new in the context of current public discourse. Like many new fields, data science hasn’t quite found its footing. The lines between it and other related fields—as far as those lines matter—are still blurry. Data science may rely on, but is not equivalent to, database architecture and administration, big data engineering, machine learning, or high-performance computing, to name a few. The core of data science doesn’t concern itself with specific database implementations or programming languages, even if these are indispensable to practitioners. The core is the interplay between data content, the goals of a given project, and the data-analytic methods used to achieve those goals. The data scientist, of course, must manage these using any software necessary, but which software and how to implement it are details that I like to imagine have been abstracted away, as if in some distant future reality. This book attempts to foresee that future in which the most common, rote, mechanical tasks of data science are stripped away, and we are left with only the core: applying the scientific method to data sets in order to achieve a project’s goals. This, the process of data science, involves software as a necessary set of tools, just as a traditional scientist might use test tubes, flasks, and a Bunsen burner. But, what matters is what’s happening on the inside: what’s happening to the data, what results we get, and why. In the following pages, I introduce a wide range of software tools, but I keep my descriptions brief. More-comprehensive introductions can always be found elsewhere, and I’m more eager to delve into what those tools can do for you, and how they can aid you in your research and development. Focus always returns to the key concepts and challenges that are unique to each project in data science, and the process of organizing and harnessing available resources and information to achieve the project’s goals. To get the most out of this book, you should be reasonably comfortable with elementary statistics—a college class or two is fine—and have some basic knowledge of a programming language. If you’re an expert in statistics, software development, or data science, you might find some parts of this book slow or trivial. That’s OK; skip or skim sections if you must. I don’t hope to replace anyone’s knowledge and experience, but I do hope to supplement them by providing a conceptual framework for working through data science projects, and by sharing some of my own experiences in a constructive way. If you’re a beginner in data science, welcome to the field! I’ve tried to describe concepts and topics throughout the book so that they’ll make sense to just about anyone with some technical aptitude. Likewise, colleagues and managers of data scientists and developers might also read this book to get a better idea of how the data science process works from an inside perspective. For every reader, I hope this book paints a vivid picture of data science as a process with many nuances, caveats, and uncertainties. The power of data science lies not in figuring out what should happen next, but in realizing what might happen next and eventually finding out what does happen next. My sincere hope is that you enjoy the book and, more importantly, that you learn some things that increase your chances of success in the future.
✔ Author(s): Brian Godsey
✔ Title: Think Like a Data Scientist: Tackle the data science process step-by-step
✔ Rating : 4.5 out of 5 base on (38 reviews)
✔ ISBN-10: 1633430278
✔ Language: English
✔ Format ebook: PDF, EPUB, Kindle, Audio, HTML and MOBI
✔ Device compatibles: Android, iOS, PC and Amazon Kindle
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