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R for Data Science

You're reading from   R for Data Science Learn and explore the fundamentals of data science with R

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781784390860
Length 364 pages
Edition 1st Edition
Languages
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Author (1):
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 Toomey Toomey
Author Profile Icon Toomey
Toomey
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Toc

Table of Contents (19) Chapters Close

R for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Data Mining Patterns 2. Data Mining Sequences FREE CHAPTER 3. Text Mining 4. Data Analysis – Regression Analysis 5. Data Analysis – Correlation 6. Data Analysis – Clustering 7. Data Visualization – R Graphics 8. Data Visualization – Plotting 9. Data Visualization – 3D 10. Machine Learning in Action 11. Predicting Events with Machine Learning 12. Supervised and Unsupervised Learning Index

Questions


Factual

  • How does using lowercase help in analyzing text?

  • Why are there so many sparse entries? Does this number make sense?

  • Determine how to order the instructors matrix.

When, how, and why?

  • How would you remove the Unicode sequences from the text?

  • In what list of terms would you be interested in finding associations?

  • How could you adjust the course credits to be inclusive of the ranges of credits?

Challenges

  • Can you determine the benefit of using word stems in the analysis?

  • Can you figure out how to display the actual text words in the dendogram rather than their index point?

  • Is there a way to convert a non-heterogeneous XML dataset to a matrix?

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