Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Chapter 5: Data Science Lifecycle
and Technologies
The earliest recorded use of the term data science goes back to 1960 and
credited to “Peter Naur”, who reportedly used the term data science as a
substitute for computer science and eventually introduced the term
“datalogy”. In 1974, Naur released his book titled “Concise Survey of
Computer Methods”, with liberal use of the term data science throughout
the book. In 1992, the contemporary definition of data science was
proposed at “The Second Japanese-French Statistics Symposium”, with the
acknowledgment of emergence of a new discipline focused primarily on
types, dimensions, and structures of data.
The term Data can be defined as “information that is processed and stored
by a computer”. Our digital world has flooded our realities with data. From
a click on a website to our smartphones tracking and recording our location
every second of the day, our world is drowning in the data. From the depth
of this humongous data, solutions to our problems that we have not even
encountered yet could be extracted. This particular process of gathering
insights from a measurable set of data using mathematical equations and
statistics can be defined as “data science”. The role of data scientists tends
to be very versatile and is often confused with a computer scientist and a
statistician. Essentially, anyone, be it a person or a company, that is willing
to dig deep to large volumes of data to gather information can be referred to
us data science practitioner. For example, companies like “Amazon” and

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“Target” keeps a track on and record of in-store and online purchases made
by the customers, to provide personalized recommendations on products
and services. The social media platforms like “Twitter” and “Instagram”,
that allow the users to list their current location, is capable of identifying
global migration patterns by analyzing the wealth of data that is handed to
them by the users themselves.