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


One common methodology is the CRISP-DM methodology (the modeling agency). The Cross Industry Standard Process for Data Mining or (CRISP-DM) model as it is known, is a process model that provides a fluid framework for devising, creating, building, testing, and deploying machine learning solutions. The process is loosely divided into six main phases. The phases can be seen in the following diagram:

CRISP-DM Methodology

Initially, the process starts with a business idea and a general consideration of the data. Each stage is briefly discussed in the following sections.

Business understanding/data understanding

The first phase looks at the machine learning solution from a business standpoint, rather than a technical standpoint. The business idea is defined, and a draft project plan is generated. Once the business idea is defined, the data understanding phase focuses on data collection and familiarity. At this point, missing data may be identified, or initial insights may be revealed. This...