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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

Product type Paperback

Published in Dec 2014

Last Updated in Feb 2025

Publisher

ISBN-13 9781784390860

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Toomey

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Table of Contents (14) Chapters

Preface

1. Data Mining Patterns FREE CHAPTER

2. Data Mining Sequences

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 can you decide whether to use Pearson, Kendall, or Spearman as a method for correlation?
When would you want to see a small degree of freedom in the correlation results?
Most of the examples used common default parameters. Explore the same examples with nondefault parameters.

When, how, and why?

Why do the polychoric functions take so long to process?
Why are the values chosen in the polychoric responses correlated?
Explain the threshold values that were calculated.

Challenges

Is there an easier way to develop the 2 x 2 matrix needed as input to the tetrachoric function?
How could you account for trends in investment vehicles when analyzing stock market returns versus fixed instruments?

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Tech Concepts

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Programming languages

73

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Authors (1)

Toomey

Toomey

Dan Toomey has been developing application software for over 20 years. He has worked in a variety of industries and companies, in roles from sole contributor to VP/CTO-level. For the last few years, he has been contracting for companies in the eastern Massachusetts area. Dan has been contracting under Dan Toomey Software Corp. Dan has also written R for Data Science, Jupyter for Data Sciences, and the Jupyter Cookbook, all with Packt.

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