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)

Markovian in R

"Statistics is the grammar of science."
– Karl Pearson

Markovian-type models are yet another option to exercise pattern discovery. The idea behind Hidden Markovian Models (HMMs) is both clever and very useful. Although some people would only recognize them for their ability to model time series, HMMs are also suitable for things such as speech recognition and computer vision.

In this chapter, readers will find a brief review of Markovian models and the HMM, a discussion about where HMMs can be applied, and of course, a practical guide, teaching the nuts and bolts of deploying HMMs through R.

In this chapter, we will cover the following topics:

  • Markovian models basics
  • Hidden Markovian Models (HMMs) basics
  • Incorporate information about the past...