November 2017
Beginner
286 pages
8h 13m
English
book
Statistics for Data Science
by James C. Mott, Rajprasath Subramanian, Shaikh Salamatullah, James D. Miller, Vijayakumar Ramdoss
Content preview from Statistics for Data Science
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Summary
In this chapter, we provided a universal definition for data mining, listed the most common techniques used by data scientists, and stated the overall objective of the efforts. Data mining was also compared to data querying and, using R, various working examples were given to illustrate certain key techniques. Finally, the concepts of dimensional reduction, frequent patterning, and sequence mining were explored.
The next chapter will be a hands-on introduction to statistical analysis of data through the eyes of a data developer, providing instructions for describing the nature of data, exploring relationships presented in data, creating a summarization model from data, proving the validly of a data model, and employing predictive ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.