# Data Visualization Lab notes ## Lecture 1 ### What is data visualization? ### Data visualization Elements ### Dimensionality reduction algorithms ### Information requires * Salience * Credibility * Legitimacy ## Lecture 2 ### History of DVL ## Lecture 3 ### Foundation of Data Visualization #### Ingradients * Data * Data represntation * Data Presentation * Data understanding - Perceiving - Interpreting - Comperhending #### Principles of Good Data Visualization * Trustworthy (Integrity + Accuracy + Legitimacy) * Accessible * Elegant - Visualization should be very simple ## Lecture 4 ### Data visualization work flow * Briefing data * Context * Vision * Skill & Resources + Frequency * Working with data * Data Acquisition * Data examination * Data Transformation * Data exploration * Stablishing Editorial thinking * Focus * Angle * Framing * Design a solution ## Lecture 5 ### Data Representation Datavis perception => Decoding elements of a graph Datavis interpretation => Decoding meaning of a graph #### Visual Encoding * Annotation * Quantity * Size * Position * Color #### Marks * Quantitative * Point => quantity as position on scale * Line * Linear spatial dimension * Quantity as variantion in size * Area * Quadratic spatial dimension * Quantity as variantion in size and position * Form * Cubic spatial dimension * Quantity as variation in volume * Position * Angle/slope * Size * Quantity * Color saturation * Color lightness * Pattern * Motion * Categorical/Relational * Symbol/Shape * Color hue * Connection/edge * Containment #### Chart types * Chart types * Bottom up * Top down * Chart classification * Categorical * Hierarchical * Relational * Temporal * Spatial ## Lecture 6 ### Charts zoo ## Lecture 7 ### Practical Tips 1. Show your data 2. Use graphics 3. Avoid chart junks 4. Data ink ratio 5. Annotation 6. Micro/Macro 7. Layers 8. Small multiples 9. Color 10. Narrative ## Lecture 8 ### Dimensionality reduction * Linear * PCA * Multi dimensional scaling * Non Linear * t-SNE * UMAP * ISOMap ## Lecture 9 ### PCA * Real vector space * Linear depdendence/independence * Linear combination * Linear transformation * Eigen vectors/value * Orthogonality ## Multi dimensional scaling * Metric * Non metric