Jake Vanderplas [Vanderplas - Python Data Science Handbook: Essential Tools for Working With Data

• 2016-11-17: First Release

This is a book about doing data science with Python, which immediatelybegs the question: what is data science? Its a surprisingly harddefinition to nail down, especially given how ubiquitous the term hasbecome. Vocal critics have variously dismissed the term as a superfluouslabel (after all, what science doesnt involve data?) or a simplebuzzword that only exists to salt rsums and catch the eye ofoverzealous tech recruiters.

In my mind, these critiques miss something important. Data science,despite its hype-laden veneer, is perhaps the best label we have for thecross-disciplinary set of skills that are becoming increasinglyimportant in many applications across industry and academia. Thiscross-disciplinary piece is key: in my mind, the best existingdefinition of data science is illustrated by ).

Figure P-1. Drew Conways Data Science Venn Diagram

While some of the intersection labels are a bit tongue-in-cheek, thisdiagram captures the essence of what I think people mean when they saydata science: it is fundamentally an interdisciplinary subject. Datascience comprises three distinct and overlapping areas: theskills of a statistician who knows how to model and summarize datasets(which are growing ever larger); the skills of a computer scientist whocan design and use algorithms to efficiently store, process, andvisualize this data; and the domain expertise what we might think ofas classical training in a subject necessary both to formulate theright questions and to put their answers in context.

With this in mind, I would encourage you to think of data science not asa new domain of knowledge to learn, but as a new set of skills that you canapply within your current area of expertise. Whether you are reportingelection results, forecasting stock returns, optimizing online adclicks, identifying microorganisms in microscope photos, seeking newclasses of astronomical objects, or working with data in any otherfield, the goal of this book is to give you the ability to ask andanswer new questions about your chosen subject area.

In my teaching both at the University of Washington and at various tech-focusedconferences and meetups, one of the most common questions I have heardis this: how should I learn Python? The people asking are generallytechnically minded students, developers, or researchers, often with analready strong background in writing code and using computational andnumerical tools. Most of these folks dont want to learn Python perse, but want to learn the language with the aim of using it as a toolfor data-intensive and computational science. While a large patchwork ofvideos, blog posts, and tutorials for this audience is available online,Ive long been frustrated by the lack of a single good answer to thisquestion; that is what inspired this book.

The book is not meant to be an introduction to Python or to programmingin general; I assume the reader has familiarity with the Pythonlanguage, including defining functions, assigning variables, callingmethods of objects, controlling the flow of a program, and other basictasks. Instead, it is meant to help Python users learn to use Pythonsdata science stack libraries such as IPython, NumPy, Pandas,Matplotlib, Scikit-Learn, and related tools to effectively store,manipulate, and gain insight from data.