Tableau Advanced Analytics with R Training

Tableau Advanced Analytics with R Training is developed for professional data visualization users who want to shift into data science and analytics to improve their business by equipping the analytical ability of R and the unique visualization capabilities of Tableau. In this course, you will get to learn various machine learning algorithms and how prescriptive, predictive, and visually appealing analytical solutions can be developed with Tableau and R. It will provide you with comprehensive examples that will help you evolve from being a data-savvy user to an advanced data analyst very conveniently.

Our enterprise training program is best for organizations and companies. It guides you to integrate Tableau's analytics with the statistical prowess of R and teaches you the right methods to utilize the CRISP-DM procedures to build a pathway for analytical investigations. You will also be able to implement different learning algorithms in R that revert values to Tableau.

Contact us to customize this class with your preferred dates, times and location.
You can call us on 1-800-961-0337 or Chat with our representative.

• Utilizing advanced calculations in Tableau and R for predictions and analytics.
• Integrating analytics with arithmetical power of R.
• Making R function calls in Tableau.
• Visualizing R functions covering Tableau by utilizing RServe.
• Utilizing CRISP-DM methodology for emergent process for analytics investigation.
• Applying unsupervised and supervised learning algorithms in R returned values to Tableau.
• Making quick decisions, cogent and data-driven by utilizing innovative analytics techniques such as predictions, association rules, forecasting and classifications.

The learning is recommended for experienced Tableau users who have a very good experience with Tableau products or willing to transit from a data users to data analysts.

• 1. Advanced Analytic with R and Tableau
• a). Installing R and R Studio
• b). Installing Rserve
• c). Environment of R
• d). Connecting to Rserve
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2. The Power of R
a). Variables
b). Vectors and Lists
c). Matrices
d). Factors
e). Data Frames
f). Control Structures
g). For Loops
h). Functions
i). Using R in Tableau
3. Methodology for Advanced Analytics
a). CRISP-DM Model – Data Preparation
b). CRISP-DM – Modeling Phase
c). CRISP-DM – Evaluation
d). CRISP-DM – Deployment
e). CRISP-DM – Process Restarted
f). CRISP-DM – Summary
g). Working with Dirty Data
h). Introduction to Dplyr
i). Summarizing Data with Dplyr
4. Prediction with R & Tableau Using Regression
a). Simple Linear Regression
b). Comparing Actual Values with Predicted Results
c). Building a Multiple Regression Model
d). Solving the Business Question
e). Sharing Data Analysis with Tableau
5. Classifying Data with Tableau
a). Understanding the Data
b). Data Preparation
c). Describing the Data
d). Modeling in R
e). Decision Trees in Tableau Using R
f). Bayesian Methods
g). Graphs
6. Advanced Analytics Using Clustering
a). What is Clustering?
b). Finding Clusters in Data
c). How Does K-Means Work?
d). Creating a Tableau Group from Cluster Results
e). Scaling
f). Clustering Without K-Means
g). Statistics For Clustering
7. Advanced Analytics with Unsupervised Learning
a). What Are Neural Networks?
b). Backpropagation and Feedforward Neural Networks
c). Evaluating a Neural Network Model
d). Lift Scores
e). Visualizing Neural Network Results
f). Modeling and Evaluating Data in Tableau
8. Interpreting Your Results for the Target Audience
a). Introduction to Decision System and Machine Learning
b). Fuzzy Logic
c). Bayesian Theory
d). Integrating a Decision System and IoT (Internet of Things) Project
e). Building Your Own Decision System-Based loT
f). Writing the Program
g). Testing
h). Enhancement