Data Visualization and Dashboards

Data Visualization and Dashboards is a specialized area focused on creating interactive, single-screen interfaces that consolidate multiple data visualizations to provide a comprehensive, at-a-glance overview of key performance indicators (KPIs) and other critical metrics. By arranging charts, graphs, and maps into a cohesive layout, dashboards transform complex datasets into easily digestible visual summaries. These tools are designed for monitoring and analysis, enabling users to track performance, identify trends, and make informed decisions quickly without needing to sift through raw data.

1. Foundations of Data Visualization
   1. Definition and Purpose of Data Visualization
      1. Communicating Information
         1. Simplifying Complex Data
         2. Enhancing Understanding
         3. Supporting Decision-Making
      2. Exploring Data
         1. Identifying Outliers
         2. Detecting Patterns
         3. Hypothesis Generation
      3. Revealing Patterns and Trends
         1. Temporal Patterns
         2. Correlations and Relationships
         3. Clusters and Groupings
   2. The Psychology of Visual Perception
      1. Gestalt Principles of Visual Perception
         1. Proximity
         2. Similarity
         3. Enclosure
         4. Closure
         5. Continuity
         6. Connection
      2. Preattentive Attributes
         1. Color
            1. Hue
            2. Saturation
            3. Brightness
         2. Form
            1. Shape
            2. Orientation
            3. Size
         3. Spatial Position
            1. Alignment
            2. Grouping
         4. Motion
      3. Cognitive Load Theory
         1. Intrinsic Load
         2. Extraneous Load
         3. Germane Load
         4. Reducing Cognitive Overload
      4. Visual Processing Speed
         1. Parallel vs Sequential Processing
         2. Attention and Focus
   3. Data Types and Structures
      1. Quantitative Data
         1. Continuous Data
            1. Interval Data
            2. Ratio Data
         2. Discrete Data
            1. Count Data
            2. Binary Data
      2. Categorical Data
         1. Nominal Data
         2. Ordinal Data
      3. Time-Series Data
         1. Timestamps and Intervals
         2. Seasonality and Trends
         3. Frequency and Granularity
      4. Hierarchical Data
         1. Tree Structures
         2. Parent-Child Relationships
      5. Geospatial Data
         1. Coordinate Systems
         2. Geographic Boundaries
         3. Spatial Relationships
      6. Network Data
         1. Nodes and Edges
         2. Graph Structures
      7. Multidimensional Data
         1. High-Dimensional Datasets
         2. Dimensionality Reduction
   4. Visual Encodings
      1. Mapping Data to Visual Attributes
         1. Data-Attribute Mapping Principles
         2. Choosing Effective Encodings
         3. Encoding Effectiveness Hierarchy
      2. Position and Length
         1. Cartesian Positioning
         2. Bar Lengths
         3. Accuracy of Position Encoding
      3. Angle and Slope
         1. Pie Slices
         2. Line Slopes
         3. Limitations of Angle Perception
      4. Area and Volume
         1. Bubble Sizes
         2. Treemap Areas
         3. Perceptual Challenges
      5. Color
         1. Hue
         2. Saturation
         3. Luminance
         4. Color Blindness Considerations
      6. Shape and Texture
         1. Differentiating Categories
         2. Indicating Status or Type
         3. Symbol Recognition
      7. Redundant Encoding
         1. Multiple Encoding Channels
         2. Reinforcing Information