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


Although most introductory data analysis texts don't even broach the topic of Bayesian methods, you, dear reader, are versed enough in this matter to start applying these techniques to real problems.

We discovered that Bayesian methods could—at least for the models in this chapter—not only allow us to answer the same kinds of questions we might use the binomial, one sample t-test, and the independent samples t-test for, but provide a much richer and more intuitive depiction of our uncertainty in our estimates. If these approaches interest you, I urge you to learn more about how to extend these to supersede other NHST tests. I also urge you to learn more about the mathematics behind MCMC. As with the last chapter, we covered much ground here. If you made it through, congratulations! This concludes the unit on confirmatory data analysis and inferential statistics. In the next unit, we will be less concerned with estimating parameters, and more interested in prediction. Last one there...