Tableau Advanced Analytics with R Course Outline
Special Note to New Hampshire Residents
This course has not yet been approved by the State's Department of Education. Please contact us to get an update as to when the class should be available in New Hampshire.
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