Data Visualization & Storytelling

Data visualization principles and human perception (pre-attentive processing, Gestalt principles), Data types and appropriate encodings (position, length, angle, area, color, shape), Chart taxonomy (categorical, temporal, spatial, hierarchical, network data), Color theory for visualization (perceptual uniformity, color blindness accessibility), Visual encoding principles (Tufte's data-ink ratio, Cleveland/McGill hierarchy).

Exploratory data analysis (EDA) workflow, Univariate distributions (histograms, box plots, violin plots, density plots), Bivariate relationships (scatterplots, heatmaps, correlation matrices), Multivariate visualization techniques (parallel coordinates, scatterplot matrices, dimensionality reduction), Outlier detection and anomaly visualization strategies.

Choropleth maps and spatial aggregation pitfalls, Proportional symbol maps, Flow maps and origin-destination visualization, Spatial autocorrelation and clustering (Moran's I), Network visualization techniques (node-link diagrams, adjacency matrices, arc diagrams), Force-directed layouts, hierarchical edge bundling.

Event handling and brushing/linking, Zooming/panning/filtering interactions, Animated transitions and small multiples, Dashboard design principles (layout, cognitive load, task prioritization), Storytelling techniques (scrollytelling, annotated charts, guided narratives), Level-of-detail management and progressive disclosure.

Narrative structures for data stories (explanatory vs. exploratory analysis), Visual hierarchy and emphasis techniques, Audience analysis and stakeholder communication, Presentation design (slide layouts, chart choice, annotation), Dashboard evaluation frameworks (USE framework, stakeholder interviews), Ethical considerations (visual manipulation, statistical fallacies, misleading scales).