Book Image

Advanced Analytics with R and Tableau

By : Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler
Book Image

Advanced Analytics with R and Tableau

By: Ruben Oliva Ramos, Jen Stirrup, Roberto Rösler

Overview of this book

Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics. By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
Table of Contents (16 chapters)
Advanced Analytics with R and Tableau
About the Authors
About the Reviewers
Customer Feedback

Solving the business question

What are we trying to do with regression? If you are trying to solve a business question that helps predict probabilities or scoring, then regression is a great place to start. Business problems that require scoring are also known as regression problems. In this example, we have scored the likelihood of the individual earning above or below fifty thousand dollars per annum.

The main objective is to create a model that we can use on other data, too. The output is a set of results, but it is also an equation that describes the relationship between a number of predictor variables and the response variable.

What do the terms mean?

For example, you could try to estimate the probability that a given person earns above or below fifty thousand dollars:

  • Error: The difference between predicted value and true value

  • Residuals: The residuals are the difference between the actual values of the variable you're predicting and predicted values from your regression--y - ŷ

For most...