Chapter 1 Concept Map
An interactive visualization showing how the concepts from Chapter 1 (Introduction to Data Science) connect and relate to each other.
View Chapter 1 Concept Map in Full Screen
How to Use This Map
- Hover over any concept to see its definition
- Click a concept to highlight all its connections
- Double-click a concept to see examples
- Filter buttons show/hide concept categories
Concept Categories
| Category | Shape | Color | Concepts |
|---|---|---|---|
| Core Concepts | Circle | Gold | Data Science, Data, Variables, Dataset |
| Data Types | Square | Blue | Numerical, Categorical, Ordinal, Nominal |
| Variable Roles | Diamond | Green | Independent, Dependent, Feature, Target |
| Workflow | Hexagon | Purple | Data Science Workflow, Problem Definition, Data Collection |
| Tools & Practices | Rectangle | Orange | Python Programming, Documentation |
| Structure | Triangle | Teal | Measurement Scales, Observation |
Relationship Types
| Edge Style | Meaning | Example |
|---|---|---|
| Solid gray | "is a type of" | Numerical → Data Types |
| Dashed blue | "contains" | Dataset → Observation |
| Dotted green | "uses" | Data Science → Python Programming |
| Thick red | "same as" | Feature ↔ Independent Variable |
Key Relationships to Understand
- Data Science works with Data - The foundation of the field
- Data is organized into Variables - How we structure information
- Variables are classified as Numerical or Categorical - The fundamental split
- Categorical splits into Ordinal (ordered) and Nominal (unordered)
- Variables can play different roles: Independent (input) vs Dependent (output)
- In ML context: Feature = Independent Variable, Target = Dependent Variable
- The Data Science Workflow guides the process from Problem Definition through Data Collection
Learning Objectives
After exploring this concept map, you should be able to:
- Identify and define all 20 core concepts from Chapter 1
- Explain how concepts relate to each other
- Classify any variable by its type and role
- Describe the data science workflow
- Understand the equivalence between traditional statistics and ML terminology