Book Image

Hands-On Data Science with R

By : Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias
Book Image

Hands-On Data Science with R

By: Vitor Bianchi Lanzetta, Doug Ortiz, Nataraj Dasgupta, Ricardo Anjoleto Farias

Overview of this book

R is the most widely used programming language, and when used in association with data science, this powerful combination will solve the complexities involved with unstructured datasets in the real world. This book covers the entire data science ecosystem for aspiring data scientists, right from zero to a level where you are confident enough to get hands-on with real-world data science problems. The book starts with an introduction to data science and introduces readers to popular R libraries for executing data science routine tasks. This book covers all the important processes in data science such as data gathering, cleaning data, and then uncovering patterns from it. You will explore algorithms such as machine learning algorithms, predictive analytical models, and finally deep learning algorithms. You will learn to run the most powerful visualization packages available in R so as to ensure that you can easily derive insights from your data. Towards the end, you will also learn how to integrate R with Spark and Hadoop and perform large-scale data analytics without much complexity.
Table of Contents (16 chapters)


In this chapter, we discussed machine learning and why it is everywhere. We also looked at supervised and unsupervised machine learning. We discussed the reason that machine learning creates its own vocabulary and how it relates to the statistical vocabulary.

Many other methods were discussed:

  • tree models, their strengths and weakness
  • essemble methods such as random forests
  • hierarchical and k-means clustering

Finally, the chapter introduced feedforward neural networks with R using the h2o package. In the next chapter, we will cover ways in which we can evaluate the quality and characteristics of various datasets in an effort to find insights from the data.