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 (10 chapters)
9
Index

References

The following is a list of recommended books where you can learn more about the topics in this chapter:

  • Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, 2004.
  • A First Course in Bayesian Statistical Methods, Peter D. Hoff. Springer, New York, 2009.
  • Bayes' Rule: A Tutorial Introduction to Bayesian Analysis, James V Stone. Sebte Press. 2013.
  • Bayesian risk management: A guide to model risk and sequential learning in financial markets, Matt Sekerke. Wiley & Sons. 2015.
  • Fuzzy logic with engineering applications, 3rd Edition, Timothy J. Ross, John Wiley & Sons. 2010.
  • A First Course in Fuzzy Logic, 3rd Edition, Hung T. Nguyen and Elbert A. Walker, CRC Press. 2006.