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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

Publisher

ISBN-13 9781784390860

Length 364 pages

Edition 1st Edition

Languages

R

Concepts

Data Science

Author (1):

Dan Toomey

View More author details

Table of Contents (14) Chapters

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?

The rest of the chapter is locked

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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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