Data science is more than just a set of tools and techniques for extracting knowledge from data sets and data streams. Data science is also a process of getting from goals and questions to real, valuable outcomes by exploring, observing, and manipulating a world of data. Traversing this world can be difficult and confusing. Software developers and non-technical folks may struggle with the uncertainty and fuzzy answers that data invariably provide, and statisticians may have trouble working with any of the multitude of relevant software tools that lie outside of their expertise. Others may not even know where to begin.

Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. This book helps you fill in conceptual knowledge gaps in the daunting fields of statistics and software development, and relates those skills to the real concerns of data science in the business world.

As you work though the many practical examples, you'll use your existing knowledge of statistics and programming to solve real problems in data science.

Key Features:

• Following the data science process data science, step by step

• Discovering the world of data as a wilderness to be explored, wrangled, and studied Learning to foresee problems at each stage of a data science project

• Dealing with the uncertainty inherent in working with data

• Understanding concepts of data, software, and statistics in ways accessible even for beginners

• Engaging in some of the most relevant best practices in software, statistics, and scientific thinking

Readers should have some familiarity with a programming language and basic statistics, but need not be experts. Important foundational concepts will be reviewed briefly in the book.

ABOUT THE TECHNOLOGY

Data science is a set of concepts and techniques for extracting meaning and clarity from stored data sets or data streams.