Chapter 1. How to Sound Like a Data Scientist

No matter which industry you work in, IT, fashion, food, or finance, there is no doubt that data affects your life and work. At some point in this week, you will either have or hear a conversation about data. News outlets are covering more and more stories about data leaks, cybercrimes, and how data can give us a glimpse into our lives. But why now? What makes this era such a hotbed for data-related industries?

In the 19^th century, the world was in the grip of the industrial age. Mankind was exploring its place in industry alongside giant mechanical inventions. Captains of industry, such as Henry Ford, recognized major market opportunities at the hands of these machines, and were able to achieve previously unimaginable profits. Of course the industrial age had its pros and cons. While mass production placed goods in the hands of more consumers, our battle with pollution also began around this time.

By the 20^th century, we were quite skilled at making huge machines; the goal now was to make them smaller and faster. The industrial age was over and was replaced by what we refer to as the information age. We started using machines to gather and store information (data) about ourselves and our environment for the purpose of understanding our universe.

Beginning in the 1940s, machines like ENIAC (considered one of, if not the first, computer) were computing math equations and running models and simulations like never before.

The ENIAC, http://ftp.arl.mil/ftp/historic-computers/

We finally had a decent lab assistant who could run the numbers better than we could! As with the industrial age, the information age brought us both the good and the bad. The good was the extraordinary pieces of technology, including mobile phones and televisions. The bad in this case was not as bad as worldwide pollution, but still left us with a problem in the 21^st century, so much data.

That's right, the information age, in its quest to procure data, has exploded the production of electronic data. Estimates show that we created about 1.8 trillion gigabytes of data in 2011 (take a moment to just think about how much that is). Just one year later, in 2012, we created over 2.8 trillion gigabytes of data! This number is only going to explode further to hit an estimated 40 trillion gigabytes of data creation in just one year by 2020. People contribute to this every time they tweet, post on Facebook, save a new resume on Microsoft Word, or just send their mom a picture through text message.

Not only are we creating data at an unprecedented rate, we are consuming it at an accelerated pace as well. Just three years ago, in 2013, the average cell phone user used under 1 GB of data a month. Today, that number is estimated to be well over 2 GB a month. We aren't just looking for the next personality quiz, what we are looking for is insight. All of this data out there, some of it has to be useful to me! And it can be!

So we, in the 21^st century, are left with a problem. We have so much data and we keep making more. We have built insanely tiny machines that collect data 24/7, and it's our job to make sense of it all. Enter the data age. This is the age when we take machines dreamed up by our 19^th century ancestors and the data created by our 20^th century counterparts and create insights and sources of knowledge that every human on Earth can benefit from. The United States created an entire new role in the government for the chief data scientist. Tech companies, such as Reddit, who up until now did not have a data scientist on their team, are now hiring them left and right. The benefit is quite obvious—using data to make accurate predictions and simulations gives us a look into our world like never before.

Sounds great, but what's the catch?

This chapter will explore the terminology and vocabulary of the modern data scientist. We will see key words and phrases that are essential in our discussion on data science throughout this book. We will also look at why we use data science and the three key domains data science is derived from before we begin to look at code in Python, the primary language used in this book:

• Basic terminology of data science
• The three domains of data science
• The basic Python syntax