December 2016
Beginner
388 pages
8h 35m
English
Content preview from Principles of Data ScienceBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Why look at these distinctions?
It might seem worthless to stop and think about what type of data we have before getting into the fun stuff, like statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.
Consider an example where we are looking at election results for a county. In the dataset of people, there is a "race" column that is denoted via an identifying number to save space. For example perhaps caucasian is denoted by 7 while Asian American is 2. Without understanding that these numbers are not actually ordered numbers like we think about them (where 7 is greater than 2 and therefore caucasian is "greater than" Asian American) we will make terrible mistakes in our analysis.Discuss ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.