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)

Measures of central tendency and dispersion

If you care to tackle a problem using the statistic's arsenal there are two tools to begin with: measures of central tendency and measures of variance. This is the starting point for most of the statistical problems. These measures are used in a thing that some would call descriptive analysis. A well done descriptive analysis may be all that you need, depending on the problem you have at hand. Think about the force continuum (and don't go straight to the Megazordstart small).

Central tendency (or average) means typical/middle value from a distribution. This is an abstract concept and we can't really measure it. Yet there are estimates that try to translate this abstract concept into an actual measure. Arithmetic mean, median, and mode are all widespread and consolidated attempts.

Even if you got yourself stuck...