Trend Spotting and Pattern Spotting: Seeing What Matters in Data Visualizations Training in Malta

• Business managers, decision makers, analysts and other analytics consumers seeking to refine their skills for understanding data visualizations
• Data scientists, data analysts, and other analytics providers seeking to enhance their data visualization skills by understanding visualization from the perspective of the readers
• Developers of data visualizations who will improve visualization skills by seeing data visualization through the eyes of the readers

• Ten key concepts of data visualization
• The most important things to look for when reading visualizations
• How to do a “quick read” of data visualizations
• How to do a “critical read” of data visualizations
• To see trends, patterns, and outliers in visual presentation of data
• To see ambiguity, distortion, and bias in visual presentation of data

Part 1: Visual Language

• About Language
  • Word Languages
  • Numeric Languages
  • Visual Languages
• Visual Language
  • Parts, Structure, and Rules
  • Visual Communication
  • Data Visualization Language
  • Interactive Visualization

Part 2: Data Visualization Concepts

• Chart and Graph Formats
  • Static
  • Interactive
  • Animated
  • Narrated (Storytelling)
• Chart and Graph Types
  • Basic Types: Line, Column, Bar, Area, Scatter, Bubble, Map, etc.
  • Beyond Basics: Tree Maps, Heat Maps, Sunbursts, Clouds, etc.
• Patterns and Trends
  • Pattern Basics: Center, Spread, Peaks, Mode, Skew, etc.
  • Patterns, Trends, and Chart Types
• Data
  • Data Sets
  • Things, Instances, and Variables
  • Data Sources and Lineage
• Visual Composition
  • Axes and Scales
  • Legends

Part 3: Quick Reading of Data Visualizations

• Finding Context
  • First Impression
  • Title
  • Axes
  • Scales
  • Legend
• Looking at the Data
  • Variables and Quantities
  • Meaning
  • Quick Read Process
  • Process Summary
  • Quick Read Exercises

Part 4: Critical Reading of Data Visualizations

• First Glance
  • From a Distance
  • Organization
  • Visual Appeal
• Source of the Chart
  • Data Sources
  • Data Analyst
  • Data Analysis
• You and the Chart
  • Format and Interaction
  • Connections
• Reading the Chart
  • Title
  • Type
  • Layout: Aspect Ratio, Legend, Colors, Patterns, Annotation, etc.
  • Data: Variables, Encodings, Relationships, etc.
  • Axes and Scales: Variables, Coordinates, Units, Intervals, etc.
  • Considering Outliers
• Finding the Meaning
  • Considering Outliers
  • Finding Patterns and Trends
  • Critical Read Exercises