Lesson 1: What Is Data?
Data is information. Every time you check the weather, count something, or record a measurement, you're working with data. In this course you'll learn to collect, organize, analyze, and visualize data using Python — the same way real data scientists do.
Key Concepts
Types of Data
Quantitative data is numbers (temperature, score, height). Categorical data is labels or groups (color, city, grade). Knowing the difference tells you which math and which chart to use.
Variables & Values
In Python a variable is a named container. x = 5 stores the number 5. scores = [90, 85, 78] stores a list of numbers. The list is one of Python's most useful data structures for working with data.
Why Analyze Data?
Raw data is hard to read. Analysis finds the story hidden inside: What's the average? What's the biggest? Is there a pattern? Visualization (charts) makes that story easy to see.
The Data Pipeline
Collect → Clean → Analyze → Visualize → Communicate. Real data scientists follow this pipeline every day. This course teaches each step.
🔬 Interactive Lab: Data Explorer
A bar chart of student test scores. Drag the bars up/down to change scores and watch the max, min, and average update live.
✅ Check Your Understanding
1. Which type of data is 'favorite color'?
2. What does len([5, 10, 15, 20]) return in Python?
3. What is the correct order of the data pipeline?