1. Data Models vs Conceptual Models
Data models are formal descriptions of data.
Conceptual models are the meanings humans attach to data.
| Data Model |
Conceptual Model |
| 1D Float |
Temperature |
| 3D Vector |
Spatial Location |
🧠Remember:
Data Model = Structure
Conceptual Model = Meaning
2. Taxonomy of Data Types
- 1D (Sets and Sequences)
- Temporal Data
- 2D Data (Maps)
- 3D Data (Shapes)
- nD Data (Relational)
- Trees (Hierarchies)
- Networks (Graphs)
🧠Memory Trick:
Sequence → Time → Map → Shape → Table → Tree → Graph
3. Qualitative vs Quantitative Data
| Qualitative |
Quantitative |
| Observed |
Measured |
| Gender |
Age |
| Country |
Height |
| Hair Color |
Weight |
🧠Shortcut:
Qualitative = WHAT
Quantitative = HOW MUCH
4. Nominal (N)
Categories without order or ranking.
Examples:
- Gender
- Country
- Hair Color
Operations:
Frequency Count, Percentage, Mode
Common Charts:
Bar Chart, Pie Chart, Treemap
5. Ordinal (O)
Categories with meaningful order.
Examples:
- Gold Medal
- Silver Medal
- Bronze Medal
Operations:
Rank, Median, Frequency Count
Common Charts:
Ordered Bar Charts, Dot Plots
6. Quantitative Data (Q)
| Type |
Description |
| Interval |
No true zero point |
| Ratio |
Has a true zero point |
Interval Example:
Temperature (°C)
Ratio Example:
Population Count
7. Dimensions vs Measures
| Dimensions |
Measures |
| Categories |
Numbers |
| Year |
Sales |
| Gender |
Revenue |
🧠Easy Rule:
Dimensions describe.
Measures calculate.
8. Census Data Example
| Variable |
Type |
| People Count |
Q-Ratio |
| Year |
Q-Interval |
| Age |
Q-Ratio |
| Sex |
Nominal |
| Marital Status |
Nominal |
9. Relational Data Model
Data is organized into tables (relations).
| Term |
Meaning |
| Row |
Record |
| Column |
Attribute |
| Table |
Relation |
10. SQL Operations
| Operation |
Purpose |
| SELECT |
Choose columns |
| WHERE |
Filter rows |
| ORDER BY |
Sort rows |
| GROUP BY |
Aggregate data |
| JOIN |
Combine tables |
11. Roll-Up and Drill-Down
Roll-Up = Summarize data
Drill-Down = Show more detail
🧠Roll-Up = Zoom Out
🧠Drill-Down = Zoom In
12. Tidy Data
Data organization standard proposed by Wickham.
- Each Variable → Column
- Each Observation → Row
- Each Observation Type → Table
Frequently tested concept in data visualization.
13. Bertin's Visual Variables
| Visual Variable |
Best For |
| Position | Quantitative |
| Size | Quantitative |
| Color Hue | Nominal |
| Value | Ordinal |
| Shape | Nominal |
🧠Position is the strongest visual encoding.
14. Expressiveness & Effectiveness
Expressiveness:
A visualization should show all facts and only the facts.
Effectiveness:
Information should be easy for humans to perceive.
🧠Expressive = Correct
🧠Effective = Easy to Read
15. Final Exam Summary
Most Important Points
- N: Nominal = Categories
- O: Ordinal = Ordered Categories
- Q: Quantitative = Numeric Values
- Dimensions: Describe data
- Measures: Analyze data
- Tidy Data:
Variable = Column,
Observation = Row
- Bertin:
Position is the strongest visual channel.
- Expressiveness:
Show correct facts.
- Effectiveness:
Easy to perceive.