Data Visualization

  1. Data Foundations for Visualization
    1. Understanding Data Types and Structures
      1. Quantitative Data
        1. Continuous Variables
          1. Interval Data
            1. Ratio Data
              1. Measurement Precision
              2. Discrete Variables
                1. Count Data
                  1. Integer Values
                    1. Bounded vs Unbounded
                  2. Categorical Data
                    1. Nominal Categories
                      1. Unordered Classifications
                        1. Binary Variables
                          1. Multiple Categories
                          2. Ordinal Categories
                            1. Ranked Classifications
                              1. Likert Scales
                                1. Educational Levels
                              2. Temporal Data
                                1. Time Series Data
                                  1. Cross-Sectional Data
                                    1. Panel Data
                                      1. Date and Time Formats
                                      2. Geospatial Data
                                        1. Point Data
                                          1. Line Data
                                            1. Polygon Data
                                              1. Coordinate Systems
                                                1. Spatial Attributes
                                                2. Multivariate Data
                                                  1. Variable Relationships
                                                    1. Correlation Structures
                                                      1. Dimensionality Considerations
                                                    2. Data Acquisition and Sourcing
                                                      1. Primary Data Collection
                                                        1. Surveys and Questionnaires
                                                          1. Experiments and Observations
                                                            1. Sensor Data Collection
                                                            2. Secondary Data Sources
                                                              1. Government Databases
                                                                1. Academic Research Data
                                                                  1. Commercial Data Providers
                                                                  2. Open Data Repositories
                                                                    1. Public Data Portals
                                                                      1. Research Data Sharing
                                                                        1. International Data Sources
                                                                        2. Web-Based Data Collection
                                                                          1. APIs and Web Services
                                                                            1. Web Scraping Techniques
                                                                              1. Social Media Data
                                                                            2. Data Quality and Assessment
                                                                              1. Data Quality Dimensions
                                                                                1. Accuracy
                                                                                  1. Completeness
                                                                                    1. Consistency
                                                                                      1. Timeliness
                                                                                        1. Validity
                                                                                        2. Missing Data Patterns
                                                                                          1. Missing Completely at Random
                                                                                            1. Missing at Random
                                                                                              1. Missing Not at Random
                                                                                              2. Outlier Detection
                                                                                                1. Statistical Methods
                                                                                                  1. Visual Identification
                                                                                                    1. Domain Knowledge Application
                                                                                                  2. Data Cleaning and Preparation
                                                                                                    1. Missing Value Treatment
                                                                                                      1. Deletion Methods
                                                                                                        1. Imputation Techniques
                                                                                                          1. Multiple Imputation
                                                                                                          2. Data Transformation
                                                                                                            1. Data Type Conversion
                                                                                                              1. Variable Recoding
                                                                                                                1. Feature Engineering
                                                                                                                  1. Derived Variables
                                                                                                                  2. Data Integration
                                                                                                                    1. Merging Datasets
                                                                                                                      1. Handling Duplicates
                                                                                                                        1. Resolving Inconsistencies
                                                                                                                        2. Aggregation and Summarization
                                                                                                                          1. Grouping Operations
                                                                                                                            1. Summary Statistics Calculation
                                                                                                                              1. Time-Based Aggregation
                                                                                                                              2. Normalization and Standardization
                                                                                                                                1. Min-Max Scaling
                                                                                                                                  1. Z-Score Standardization
                                                                                                                                    1. Robust Scaling Methods
                                                                                                                                  2. Data Structure for Visualization
                                                                                                                                    1. Tidy Data Principles
                                                                                                                                      1. Variables as Columns
                                                                                                                                        1. Observations as Rows
                                                                                                                                          1. Values in Cells
                                                                                                                                          2. Data Format Considerations
                                                                                                                                            1. Wide Format
                                                                                                                                              1. Long Format
                                                                                                                                                1. Reshaping Operations
                                                                                                                                                2. Hierarchical Data Structures
                                                                                                                                                  1. Nested Data
                                                                                                                                                    1. Tree Structures
                                                                                                                                                      1. Network Data

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                                                                                                                                                  1. Introduction to Data Visualization

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                                                                                                                                                  3. Principles of Visual Perception