Advanced Analytics with R and Tableau Training

How To Take This Class

Live Instructor-Led Online Class

Cost: $950.00

  • Open enrollment class for individuals
  • Live class with an instructor
  • Free class retakes forever!
  • Six months of instructor email support
  • Hands-on exercises and student labs
  • Classes never cancelled due to low enrollment
  • Money-back guarantee

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Onsite or Offsite Group Training

Cost: Based on number of students

  • For groups as small as 3 people
  • Class Held at our location or yours
  • Hands-on exercises and student labs
  • Customization at no extra charge
  • Six months of instructor email support
  • All-inclusive pricing
  • Money-back guarantee
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Course Duration

2 Days

Course Description

In this Advanced Analytics with R and Tableau Training course, students who have mastered data visualization using Tableau will move on to perform more advanced data analytics using R and Tableau. Students will learn how to use analytics along with data science concepts to enhance their business processes by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Students will learn about a range of machine learning algorithms and see how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. Hands-on exercises in this course will help students transition from being data-savvy Tableau users to Data Analysts using sound statistical tools to perform advanced analytics.

Course Objectives

Upon successful completion of this course, students will be able to:
  • Integrate Tableau's analytics with the industry-standard, statistical prowess of R.
  • Make R function calls in Tableau, and visualize R functions with Tableau using RServe.
  • Use the CRISP-DM methodology to create a roadmap for analytics investigations.
  • Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.
  • Use advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.

Course Audience

This course is designed for students with significant experience using Tableau to create data visualizations who want to utilize Tableau and R to perform advanced analysis and prediction.

Course Prerequisites

To receive the most benefit from this class, students should have attended, or be familiar with, most of the concepts covered in the following Tableau courses:

Tableau Training

Advanced Tableau Training

Course Syllabus

  1. Advanced Analytics with R and Tableau
    • Installing R for Windows
    • RStudio
    • Implementing the scripts for the class
    • Tableau and R connectivity using Rserve
  2. The Power of R
    • Core essentials of R programming
    • Data structures in R
    • Data frames
    • Control structures in R
    • For loops and vectorization in R
    • Functions
    • Creating your own function
    • Making R run more efficiently in Tableau
  3. Advanced Analytics Using Tableau and R
    • Industry standard methodologies for analytics
    • CRISP-DM
    • Team Data Science Process
    • Working with dirty data
    • Introduction to dplyr
  4. Prediction with R and Tableau Using Regression
    • Getting started with regression
    • Comparing actual values with predicted results
    • Getting started with multiple regression
    • Sharing our data analysis using Tableau
  5. Classifying Data with Tableau
    • Defining Business Objectives
    • Understanding the data
    • Modeling in R
    • Model deployment
    • Decision trees in Tableau using R
    • Bayesian methods
    • Graphs
  6. Advanced Analytics Using Clustering
    • What is Clustering?
    • Finding clusters in data
    • Clustering in Tableau
    • Clustering example in Tableau
    • Interpreting your results
    • How Clustering Works in Tableau
    • Scaling
    • Clustering without using k-means
    • Statistics for Clustering
    • Introduction to R
  7. Advanced Analytics with Neural Networks
    • What are neural networks?
    • Backpropagation and Feedforward neural networks
    • Evaluating a neural network model
    • Neural network performance measures
    • Visualizing neural network results
    • Neural network in R
    • Modeling and evaluating data in Tableau
  8. Interpreting Your Results for Your Audience
    • Introduction to decision system and machine learning
    • Decision system-based Bayesian
    • Bayesian Theory
    • Fuzzy logic
    • Building a simple decision system-based Bayesian theory
    • Integrating a decision system and IoT project
    • Building your own decision system-based IoT