Tableau Advanced Analytics with R Course Outline
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.
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.
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.
What You Will Learn
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.
Chapter 1 - Advanced Analytic with R and Tableau
Installing R and R Studio
Environment of R
Connecting to Rserve
Chapter 2 – The Power of R
Vectors and Lists
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
Describing the Data
Modeling in R
Decision Trees in Tableau Using R
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
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
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
Integrating a Decision System and IoT (Internet of Things) Project
Building Your Own Decision System-Based loT
Writing the Program
View outline in Word