Tableau Advanced Analytics with R

Moving from data visualization into deeper, more advanced analytics? This course 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.

Retail Price: $1,950.00

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Course Days: 2


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About this Course

Moving from data visualization into deeper, more advanced analytics? This course 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.

Together, Tableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation. 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.

Audience Profile

This class is for Tableau users who are comfortable with the product and are ready to transition to from being a data-savvy user to being a data analyst using sound statistical tools to perform advanced analytics.

At Course Completion

Upon completing this course, students will be able to:

  • Integrate Tableaus analytics with the industry-standard, statistical prowess of R.
  • Make R function calls in Tableau, visualizing 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 that return values to Tableau.
  • Get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.
  • Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau R calculated field functions.

Prerequisites

Before attending this course, students should have taken or be familiar with the contents presented in Tableau Desktop Level 1: Introduction and the Tableau Desktop 2: Intermediate courses.


Course Outline

Chapter 1 - Advanced Analytic with R and Tableau

  • Installing R and R Studio
  • Installing Rserve
  • Environment of R
  • Connecting to Rserve

Chapter 2 – The Power of R

  • Variables
  • Vectors and Lists
  • Matrices
  • Factors
  • Data Frames
  • Control Structures
  • For Loops
  • Functions
  • Using R in Tableau

Chapter 3 – Methodology for Advanced Analytics

  • CRISP-DM Model – Data Preparation
  • CRISP-DM – Modeling Phase
  • CRISP-DM – Evaluation
  • CRISP-DM – Deployment
  • CRISP-DM – Process Restarted
  • CRISP-DM – Summary
  • Working with Dirty Data
  • Introduction to Dplyr
  • Summarizing Data with Dplyr

Chapter 4 – Prediction with R and Tableau Using Regression

  • Simple Linear Regression
  • Comparing Actual Values with Predicted Results
  • Building a Multiple Regression Model
  • Solving the Business Question
  • Sharing Data Analysis with Tableau

Chapter 5 – Classifying Data With Tableau

  • Understanding the Data
  • Data Preparation
  • Describing the Data
  • Modeling in R
  • Decision Trees in Tableau Using R
  • Bayesian Methods
  • Graphs

Chapter 6 – Advanced Analytics Using Clustering

  • What is Clustering?
  • Finding Clusters in Data
  • How Does K-Means Work?
  • Creating a Tableau Group from Cluster Results
  • Scaling
  • Clustering Without K-Means
  • Statistics For Clustering

Chapter 7 – Advanced Analytics With Unsupervised Learning

  • What Are Neural Networks?
  • Backpropagation and Feedforward Neural Networks
  • Evaluating a Neural Network Model
  • Lift Scores
  • Visualizing Neural Network Results
  • Modeling and Evaluating Data in Tableau

Chapter 8 – Interpreting Your Results For Your Audience

  • Introduction to Decision System and Machine Learning
  • Fuzzy Logic
  • Bayesian Theory
  • Integrating a Decision System and IoT (Internet of Things) Project
  • Building Your Own Decision System-Based loT
  • Writing the Program
  • Testing
  • Enhancement


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