Data Science Terminology
We live in the Data Age. No matter the industry you work in, be it IT, fashion, food, or finance, there is no doubt that data affects your life and work. At some point today, this week, or this month, you will either have or hear about a conversation about data. News outlets are covering more and more stories about data leaks, cybercrimes, and how modern artificial intelligence and machine learning algorithms are changing the way we work and live.
In this book, we will attempt to cover, to put it simply, the principles of how we should interpret, interact with, manipulate, and utilize data. We will attempt to cover the principles of data science. Before we can begin covering such a huge topic, first, we have to build a solid foundation below our feet.
To begin our journey, this chapter will explore the terminology and vocabulary of the modern data scientist. We will learn keywords and phrases that will be essential in our discussion of data science throughout this book. We will also learn why we use data science and learn about the three key domains that data science is derived from before we begin to look at the code in Python, the primary language that will be used in this book.
This chapter will cover the following topics:
- The basic terminology of data science
- The three domains of data science
- The basic Python syntax
