Book Image

Statistics for Data Science

Book Image

Statistics for Data Science

Overview of this book

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Using R to apply machine learning techniques to a database


We've used the R programming language pretty much throughout this book since it is used by most data scientists and is very easy for people just getting started in statistics to comprehend. In this chapter, we'll again use R, this time to suggest how machine learning techniques might be applicable to a data or database developer.

We'll use a post offered by Will Stanton, a data scientist, to get us started. In his post, he offers a clever example of creating a simple classification model in R, using the caret package.

The R caret package Will uses in his example is very easy to use, containing wrapper functions that allow you to use the exact same functions for training and predicting with dozens of different algorithms. On top of that, it includes sophisticated, built-in methods for evaluating the effectiveness of the predictions you get from the model.

In this example (although it's perhaps a bit morbid), the task at hand is to build...