Enhancing Tableau With Python and R Perform Advanced Analytics and Create Compelling Data Stories Using Python and R Scripts

Take your data analysis, data pipelines, and data visualizations, to the next level by including Python and R scripts in your Tableau workbooks

Key Features

• Create end-to-end Python and R statistical extensions ready for Tableau
• Build predictive applications using the Python and R languages and their features specifically for Tableau
• Ingest, visualize and transform your data with Python and R in Tableau

Book Description

Enhancing Tableau with Python and R aims to help extend Tableau's capabilities for faster, smarter, and more powerful analytics.

You'll begin by setting up the TabPy and RServe analytical extensions for Tableau and thereafter, build your first custom functions to cleanse, reshape, and encode data using the most widely used frameworks. Moving ahead, you will learn how to gain more insights by making your analysis more robust, digestible and explainable with the help of detecting outliers, minimizing biases and selecting the most relevant features. Then, you will strengthen your storytelling by augmenting data, applying predictive functions and integrating models in your visualizations and performing key tasks like training a segmentation model, picking the right parameters for cluster analysis, and extracting data from API sources. Furthermore, you will explore the Python and R TensorFlow interface for forecasting, training deep learning models and natural language processing. Lastly, you will get well versed in pairing Tableau with cloud data science and big data environments like Jupyter Notebooks, Heroku, AWS S3 and Apache Spark.

By the end of this book, you will be able to code, integrate and run your own predictive analytics and data preparation pipelines using Python and R and its end-to-end environment within Tableau.

What you will learn

• Cleanse, reshape, and standarize data with Prep Script steps in Python and R
• Discover and interpret complex patterns by developing statistical scripts
• Apply data encoding and transformation techniques to Tableau fields
• Enhance Tableau visualizations with external data ingestion using TabPy
• Develop descriptive analytics and factor analysis functions in Python and R
• Perform feature extraction with Python and R scripts
• Explore deep learning with time-series forecasting using Python and R
• Pair big data and data science environments with Tableau using APIs

Who This Book Is For

This book is for business analysts, business intelligence professionals, data engineers, and data scientists who already use Tableau and want to add more value to their analysis using Python and R. Tableau as well as Python and R users who want to enhance their analysis with the data transformation and visualization capabilities of Tableau will benefit from this book. Working knowledge of Tableau is required to make the most of this book. Basic knowledge of Python and R will also be helpful.

Table of Contents

1. Setting up and running Python and R scripts in Tableau
2. Extracting features in your data with Python and R scripts
3. Reshaping and pre-processing your data with Python and R scripts
4. Text Analytics in Tableau with Python Scripts
5. Ingesting API data with Python scripts
6. Too good to be true? Presenting Usual and Unusual Outcomes
7. Maximizing outcomes with factor analysis
8. Identifying best customers with Python and R clustering functions
9. Predicting churn with Python and R Machine-Learning functions
10. Building product recommendations with Python and R functions
11. Time-series forecasting with Python and R deep-learning functions

(N.B. Additional chapters to be confirmed upon publication