Data-Visualization-Including-Machine-Learning-Concepts-using-Tableau

Data Visualization Including Machine Learning Concepts using Tableau

Course Objectives

Tableau is one of the most widely used data visualization, reporting, and business intelligence tools in the world and has regularly been ranked as a leader in the Gartner Magic Quadrant. Tableau is simple to use and has extensive visualization capabilities that make it the reporting tool of choice for enterprises around the world.

The Tableau training including machine learning concepts organized by BISP help on acquiring important skills you can use on the job such as using statistical tools and analytics, creating reports and dashboards, and mastering the many aspects of data visualization and it helps in clearing Tableau Desktop 10 Certification.

Course Outline

Introduction to Data Visualization with Tableau

• What is Data Visualization?
• Identify the prerequisites, goal, objectives, methodology, and Goal.

Architecture of Tableau

• What is Tableau?
• Features of Tableau.
• Installation of Tableau Desktop.
• The interface of Tableau(Layout/Toolbars/Data Pane/Analytics Pane etc).

Managing Data Sources

• Connection to various data sources
• Mysql Database connectivity and tables
• ODBC and other sources connectivity

Introduction to Visual Analytics with Tableau.

• Introduction to various charts
• Bar Charts, Line Graphs, Pie Charts
• Maps, Scatter Plots, Gantt Charts, Bubble Charts
• Treemaps and Box-and-Whisker Plots

Deep dive into Visualization

• Highlighting Tables, Heat Maps.
• Circle Plots
• Side By Sidebar charts, Dual Axis Charts, Area Charts.
• Waterfall Charts, Bump Chart, Word Cloud Chart, Pareto Charts.
• Multiple fields: continuous and discrete visualization

Data Organization and Scripting

• Calculated Metrics, Apply Sorting on Dimension and Measures.
• Calculating Total and Subtotals.
• Various Aggregated measures, Percentages.
• Data Spotlighting, Summary Card, Date and Time Functions.
• Logical Functions.
• Hierarchies, Groups, and Bins.
• Prediction with extrapolation

Working with Time Dimension

• Quick Table Calculations.
• Custom Table Calculations,
• YTD, Parallel Period, Moving Averages.
• Running totals, Window Averages,
• Trend Lines and Predictive Models

Data Incremental Loading and Data Blending

• Data Blending from multiple source systems Creating Incremental Loads
• Creating File Extractions, Parameters

Python: Environment Setup and Essentials

• Python Environment Installation and Configuration.
• Jupyter Notebook Installation
• Jupyter Notebook Introduction
• Variable Assignment
• Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
• Creating, accessing, and slicing tuples
• Creating, accessing, and slicing lists
• Creating, viewing, accessing, and modifying dicts
• Creating and using operations on sets
• Basic Operators: 'in', '+', '*'
• Functions
• Control Flow

Data Manipulation with Python (Pandas)

• Introduction to Pandas
• Data Structures
• Series
• DataFrame
• Missing Values
• Data Operations
• Data Standardization
• Pandas File Read and Write Support
• SQL Operation

Machine Learning with Python (ScikitLearn)

• Introduction to Machine Learning
• Machine Learning Approach
• How Supervised and Unsupervised Learning Models Work
• Scikit-Learn
• Supervised Learning Models - Linear Regression
• K Nearest Neighbors (K-NN) Model
• Unsupervised Learning Models: Clustering
• Unsupervised Learning Models: Dimensionality Reduction
• Model Persistence

Apply Machine learning concepts in Tableau

• Linear Regression.
• Clustering (K -Means)
• Data Forecasting
• Pearson correlation coefficient
• Integrate Tableau with Python using Tabby server.

Tableau Server Comprehensive

• Single Installer
• Worker Installer
• Authentication
• Processes
• Data Server
• Create the First Admin User
• Create Projects and Groups
• Assign Permissions to Projects
• Data Connections
• Metadata management
• Connecting to data with Data Server
• Updating data sources