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Tableau Advanced Analytics with R |
OverviewMoving 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 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. 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. 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. 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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