Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
RefresheR
The Shape of Data
Describing Relationships
Probability
Using Data To Reason About The World
Testing Hypotheses
Bayesian Methods
The Bootstrap
Predicting Continuous Variables
Predicting Categorical Variables
Predicting Changes with Time
Sources of Data
Dealing with Missing Data
Dealing with Messy Data
Dealing with Large Data
Working with Popular R Packages
Reproducibility and Best Practices
Other Books You May Enjoy
Index

Summary

In this chapter, we took a detour through probability land. You learned some basic laws of probability, sample spaces, and conditional independence. You also learned how to derive Bayes' Theorem and learned that it provides the recipe to update hypotheses in the light of new evidence.

We also touched on the two primary interpretations of probability. In future chapters, we will be employing techniques from both these approaches.

We concluded with an introduction to sampling from distributions and used two—binomial and normal—distributions to answer interesting non-trivial questions about probability.

This chapter laid the important foundation that supports confirmatory data analysis. Making and checking inferences based on data is all about probability and, at this point, we know enough to move on to have a great time testing hypotheses with data!