We begin this chapter with a discussion of statistical inference and statistical thinking. Next we explore what we feel every data scientist should do once they’ve gotten data in hand for any data-related project: exploratory data analysis (EDA).
From there, we move into looking at what we’re defining as the data science process in a little more detail. We’ll end with a thought experiment and a case study.
Big Data is a vague term, used loosely, if often, these days. But put simply, the catchall phrase means three things. First, it is a bundle of technologies. Second, it is a potential revolution in measurement. And third, it is a point of view, or philosophy, about how decisions will be—and perhaps should be—made in the future.
— Steve Lohr The New York Times
When you’re developing your skill set as a data scientist, certain foundational pieces need to be in place first—statistics, linear algebra, some programming. Even once you have those pieces, part of the challenge is that you will be developing several skill sets in parallel simultaneously—data preparation and munging, modeling, coding, visualization, and communication—that are interdependent. As we progress through the book, these threads will be intertwined. That said, we need to start somewhere, and will begin by getting grounded in statistical inference.
We expect the readers of this book to ...