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In 2006, Clive Humbly, UK Mathematician, and architect of Tesco’s Clubcard coined the phrase “Data is the new oil. He said the following:

*”**Data is the new oil. It’s valuable, but if unrefined it cannot be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so, must data be broken down, analyzed for it to have value.”*

The iPhone revolution, growth of the mobile economy, advancements in Big Data technology has created a perfect storm. In 2012, HBR published an article that put Data Scientists on the radar.

The article Data Scientist: The Sexiest Job of the 21st Century labeled this “new breed” of people; a hybrid of data hacker, analyst, communicator, and trusted adviser. Every organization is now making attempts to be more data-driven. Machine learning techniques have helped them in this endeavor. I realize that a lot of the material out there is too technical and difficult to understand. In this series of articles, my aim is to simplify Data Science. I will take a cue from the Stanford course/book (An Introduction to Statistical Learning). This attempt is to make Data Science easy to understand for everyone.

In this article, I will begin by covering fundamental principles, general process and types of problems in Data Science.

Data Science is a multi-disciplinary field. It is the intersection between the following domains:

- Business Knowledge
- Statistical Learning aka Machine Learning
- Computer Programming

The focus of this series will be to simplify the Machine Learning aspect of Data Science. In this article, I will begin by covering principles, general process and types of problems in Data Science.

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