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© 2017

Pro Tableau

A Step-by-Step Guide

Benefits

  • Showcases the connectivity of tableau to different Big Data sources such as Hadoop and various NoSQL databases as well as to multi-dimensional data structures such as SSAS cube, PowerPivot etc

  • Our book will have in its scope the implementation of data mining algorithms such as association rule mining, clustering, etc. leveraging the integration of Tableau with R

  • Focuses on topics as required from a developer perspective and share industry best tips and techniques

Book
  • 48k Downloads

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Seema Acharya, Subhashini Chellappan
    Pages 1-48
  3. Seema Acharya, Subhashini Chellappan
    Pages 49-120
  4. Seema Acharya, Subhashini Chellappan
    Pages 121-236
  5. Seema Acharya, Subhashini Chellappan
    Pages 237-319
  6. Seema Acharya, Subhashini Chellappan
    Pages 321-431
  7. Seema Acharya, Subhashini Chellappan
    Pages 433-493
  8. Seema Acharya, Subhashini Chellappan
    Pages 495-545
  9. Seema Acharya, Subhashini Chellappan
    Pages 547-663
  10. Seema Acharya, Subhashini Chellappan
    Pages 665-727
  11. Seema Acharya, Subhashini Chellappan
    Pages 729-793
  12. Seema Acharya, Subhashini Chellappan
    Pages 795-833
  13. Back Matter
    Pages 835-845

About this book

Introduction

Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards.  This book will help those familiar with Tableau software chart their journey to being a visualization expert.

Pro Tableau demonstrates the power of visual analytics and teaches you how to:

• Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc.

• Write your own custom SQL, etc.

• Perform statistical analysis in Tableau using R

• Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.)

What you’ll learn:

• How to connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc.

• How to leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc.

• How to integrate Tableau with R

• How to tell a compelling story with data by creating highly interactive dashboards

Keywords

Tableau Data Visualisation R Quick Table calculations String Calculation Number Calculation Data Calculation Chart Forms Integration with R Measure Names Measure Data

Authors and affiliations

  1. 1.PuneIndia
  2. 2.BangaloreIndia

About the authors

Seema Acharya is a Lead Principal with the Education, Training and Assessment department of Infosys Limited. She is an educator by choice and vocation, and has rich experience in both academia and the software industry. She is also the author of the books, “Fundamentals of Business Analytics”, ISBN: 978-81-265-3203-2, publisher – Wiley India and “Big Data and Analytics”, ISBN: 9788126554782, publisher – Wiley India. She has co-authored a paper on “Collaborative Engineering Competency Development” for ASEE (American Society for Engineering Education). She holds the patent on “Method and system for automatically generating questions for a programming language”. Her areas of interest and expertise are centered on Business Intelligence, Big Data and Analytics technologies such as Data Warehousing, Data Mining, Data Analytics, Text Mining and Data Visualisation.

Subhashini Chellappan is a Software Engineering Team Lead with the talent division of Accenture. She has rich experience in both academia and the software industry.
She has published couple of papers in various Journals and Conferences.
Her areas of interest and expertise are centered on Business Intelligence, Big Data and Analytics technologies such as Hadoop, NoSQL Databases, Spark and Machine Learning.

Bibliographic information

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