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


Data science requires a process to ensure that the project is successful. As we have seen from the previous frameworks, it requires many moving parts from the extraction of timely data from diverse data sources, building and testing the models, and then deploying those models to aid in or to automate day-to-day decision making processes. Otherwise, the project can easily fall through the gaps in this data so that the organization is right where they started: data rich, information poor.

In this example, we have covered the CRISP-DM methodology and the TDSP methodology. Each of these stages has the data preparation stage clearly marked out. In order to follow this sequence, we have started with a focus on the data preparation stage using the dplyr package in R. We have cleaned some data and compared the results between the dirty and clean data.