Tag: Visual Analytics

• Introduction
• Connecting to data
• Simplifying and sorting your data
• Organizing your data
• Slicing your data by date
• Using multiple measures in a view
• Showing the relationship between numerical values
• Mapping data geographically
• Viewing specific values
• Customizing your data
• Analyzing data with quick table calculations
• Showing breakdowns of the whole
• Highlighting data with reference lines
• Making your views available

Introduction

Working with Data

• Connecting data sources
• Connecting various databases
• Different data connection types
• Working with multiple data sources and data blending

Visual Analytics

• Creating basic data visualizations
• Sorting, filtering, and organizing data
• Using multiple measures on the same axis
• Showing relationships between values
• Grouping and forecasting
• Using charts

Mapping Data

• Geographical data mapping
• Tableau geocoding
• Advanced mapping and using background images

Basic Calculations and Aggregations

• Using parameters, measures, and dimensions
• Using sets

Using Dashboards

• Quick filters
• Actions
• Parameters

Advanced Analytics

• Advance calculations
• Using funnel charts
• Using control charts
• Using Bump charts

Tips, Tricks, and Best Practices

• Using parameters, calculations, sorting, filtering, and other functions
• Best practices when using Tableau
• Best practices when using graphs
• Best practices for data visualization

Duration

14 hours (usually 2 days including breaks)

Requirements

Experience of analysis, statistics and producing data an advantage

Overview

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake.

Course Outline

1. Introduction to Visual Analytics
   • 5 Principles of Data Visualisation
   • Tables vs charts
   • What makes visualisations effective
   • Gestalt Principles of Visual Perception
2. Types of charts and how to choose the right one
   • Common types of charts
   • Choosing the right chart for your data
   • Understanding your audience
   • Handling missing data
3. Advanced charts
   • Sankey
   • Radar
   • Treemap
   • Heatmap
   • Boxplot, violin plot
   • Choosing the right chart for your data
   • Choosing the right chart for your audience
   • Eliminating clutter from charts
4. Storytelling with data
   • The importance of storytelling
   • Building a narrative structure
   • Drawing attention
   • Including call to action
5. Creating dashboards and infographics
   • Exploratory vs explanatory analysis
   • How to convey your message
   • Live presentation vs report
   • Visualisations that are simple, informative and engaging
   • The characteristics of a good dashboard
   • The characteristics of a good infographic
6. Common mistakes and misleading charts
   • Charts that should be avoided
   • How we are being deceived by colour, scale and size
7. Visual analytics case studies