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# System prepended metadata

title: Data Visualization Lab notes

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# 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