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Developing Analytic Talent: Becoming a Data Scientist by Vincent Granville

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CHAPTER1

What Is Data Science?

Sometimes, understanding what something is includes having a clear picture of what it is not. Understanding data science is no exception. Thus, this chapter begins by investigating what data science is not, because the term has been much abused and a lot of hype surrounds big data and data science. You will first consider the difference between true data science and fake data science. Next, you will learn how new data science training has evolved from traditional university degree programs. Then you will review several examples of how modern data science can be used in real-world scenarios.

Finally, you will review the history of data science and its evolution from computer science, business optimization, and statistics into modern data science and its trends. At the end of the chapter, you will find a Q&A section from recent discussions I've had that illustrate the conflicts between data scientists, data architects, and business analysts.

This chapter asks more questions than it answers, but you will find the answers discussed in more detail in subsequent chapters. The purpose of this approach is for you to become familiar with how data scientists think, what is important in the big data industry today, what is becoming obsolete, and what people interested in a data science career don't need to learn. For instance, you need to know statistics, computer science, and machine learning, but not everything from these domains. You don't need to know the ...

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