✅ Statistics Basics for Data Science 📈📊

👉 Statistics helps you understand, analyze, and make decisions from data.

🔹 1. What is Statistics?
Statistics = Collecting, analyzing, and interpreting data
👉 Used in:
✔ Data analysis
✔ Machine learning
✔ Business decisions

🔥 2. Types of Statistics
✅ Descriptive Statistics
👉 Summarize data
Examples:
✔ Mean
✔ Median
✔ Mode

✅ Inferential Statistics
👉 Make predictions from data
Examples:
✔ Hypothesis testing
✔ Confidence intervals

🔹 3. Measures of Central Tendency ⭐
✅ Mean (Average)

import numpy as np 
np.mean([10,20,30]) 

👉 Output: 20

✅ Median (Middle Value)

np.median([10,20,30]) 

👉 Output: 20

✅ Mode (Most Frequent Value)
Example:
[1,2,2,3] → Mode = 2

🔹 4. Measures of Dispersion ⭐
✅ Range
max - min

✅ Variance
👉 Spread of data

np.var([10,20,30]) 

✅ Standard Deviation (Very Important ⭐)

np.std([10,20,30]) 

👉 Shows how much data deviates from mean.

🔹 5. Data Distribution
✅ Normal Distribution (Bell Curve) 🔔
✔ Most values around mean
✔ Symmetrical

🔹 6. Why Statistics is Important?
✔ Helps understand data deeply
✔ Required for ML algorithms
✔ Improves decision making

🎯 Today’s Goal
✔ Understand mean, median, mode
✔ Learn variance standard deviation
✔ Understand data distribution

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