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Hands-On Data Science with R

You're reading from   Hands-On Data Science with R Techniques to perform data manipulation and mining to build smart analytical models using R

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789139402
Length 420 pages
Edition 1st Edition
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Authors (4):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
Vitor Bianchi Lanzetta Vitor Bianchi Lanzetta
Author Profile Icon Vitor Bianchi Lanzetta
Vitor Bianchi Lanzetta
Doug Ortiz Doug Ortiz
Author Profile Icon Doug Ortiz
Doug Ortiz
Ricardo Anjoleto Farias Ricardo Anjoleto Farias
Author Profile Icon Ricardo Anjoleto Farias
Ricardo Anjoleto Farias
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Data Science and R FREE CHAPTER 2. Descriptive and Inferential Statistics 3. Data Wrangling with R 4. KDD, Data Mining, and Text Mining 5. Data Analysis with R 6. Machine Learning with R 7. Forecasting and ML App with R 8. Neural Networks and Deep Learning 9. Markovian in R 10. Visualizing Data 11. Going to Production with R 12. Large Scale Data Analytics with Hadoop 13. R on Cloud 14. The Road Ahead 15. Other Books You May Enjoy

KDD, Data Mining, and Text Mining

"Certainty of death. Small chance of success. What are we waiting for?"
- Gimli, son of Gloin

Aside from being a buzzword, data mining is the analysis step from Knowledge Discovery in Databases (KDDs), which is concerned with uncovering hidden patterns from huge unstructured datasets. The term data mining doesn't define a single method, but a broad collection of used methods. Those methods range from linear regressions and clustering techniques, all the way to visualizations, random forests, and artificial intelligence methods.

You may have already noticed, but it's not that easy to set apart what data mining is from data science. I mostly think about data mining as something that data scientists are doing to big data (another...

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