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 an overview of the fundamentals of the different kinds or types of statistical data cleansing. Then, using the R programming language, we illustrated various working examples, showing each of the best or commonly used data cleansing techniques.
We also introduced the concepts of data transformation, deductive correction, and deterministic imputation.
In the next chapter, we will dive deep into the topic of what data mining is and why it is important, and use R for the most common statistical data mining methods: dimensional reduction, frequent patterns, and sequences.
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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.