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  • Book Overview & Buying R for Data Science
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R for Data Science

R for Data Science

By : Toomey
3.4 (5)
Buy this Book
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R for Data Science

R for Data Science

3.4 (5)
By: Toomey
Buy this Book

Overview of this book

If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Mining Patterns
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1. Data Mining Patterns
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Cluster analysis
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Anomaly detection
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Association rules
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Questions
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Summary
2
2. Data Mining Sequences
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2. Data Mining Sequences
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Patterns
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Questions
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Summary
3
3. Text Mining
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3. Text Mining
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Packages
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Questions
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Summary
4
4. Data Analysis – Regression Analysis
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4. Data Analysis – Regression Analysis
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Packages
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Questions
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Summary
5
5. Data Analysis – Correlation
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5. Data Analysis – Correlation
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Packages
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Questions
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Summary
6
6. Data Analysis – Clustering
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6. Data Analysis – Clustering
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Packages
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K-means clustering
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Questions
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Summary
7
7. Data Visualization – R Graphics
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7. Data Visualization – R Graphics
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Packages
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Questions
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Summary
8
8. Data Visualization – Plotting
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8. Data Visualization – Plotting
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Packages
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Scatter plots
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Bar charts and plots
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Questions
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Summary
9
9. Data Visualization – 3D
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9. Data Visualization – 3D
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Packages
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Generating 3D graphics
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Questions
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Summary
10
10. Machine Learning in Action
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10. Machine Learning in Action
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Packages
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Dataset
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Questions
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Summary
11
11. Predicting Events with Machine Learning
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11. Predicting Events with Machine Learning
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Automatic forecasting packages
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Questions
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Summary
12
12. Supervised and Unsupervised Learning
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12. Supervised and Unsupervised Learning
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Packages
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Questions
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Summary
13
Index
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Index

Summary

In this chapter, we discussed different methods of determining patterns in data. We found patterns in a dataset using the eclat function looking for similar patterns in a population. We used a TraMineR to find frequent sets of items in a market basket. We used apriori rules to determine associations among items in a market basket. We used TraMineR to determine sequences of career transition among adults and visualized the same with extensive graphics features available for sequence data. Finally, we examined the similarities and differences between the sequences using seqdist.

In the next chapter, we will cover text mining or examining datasets that are text-based, rather than numerical or categorical.

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Tech Concepts
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Programming languages
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R for Data Science
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