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)

Statistical hypothesis testing

Imagine that you have estimated something about your data that you don't know for sure. Assuming that what you have imagined is true, what are the chances of getting the estimations that you found or even more extreme values? This is hypothesis testing. Statistical hypothesis testing (or simply, hypothesis testing, HT) is the name given to a set of well-known, practical methods used to make inferences with statistics. As long you have data and you're willing to make some inferences about it, the odds are that HT is the way to go. It can work out a great variety of real-world problems.

Although it's usually better to work with experimental data, it's also possible to statistically test hypotheses using observational data as well. Exhibit A: economists all over the world are doing it. A medical treatment's effectiveness, production...