Statistics for Data Science
📘 Data Science
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📅 Nov 14, 2025
⏱ Estimated reading time: 1 min
Introduction
Statistics is the foundation of Data Science. It helps understand data patterns and build accurate machine learning models.
1. Measures of Central Tendency
df["age"].mean()
df["salary"].median()
df["score"].mode()
2. Measures of Spread
- Variance
- Standard Deviation
- Range
- IQR
df["age"].std()
df["age"].var()
3. Probability
Probability measures the chance of an event occurring.
4. Distributions
- Normal Distribution
- Binomial
- Poisson
5. Correlation
df.corr()
6. Hypothesis Testing
- Z-test
- T-test
- Chi-square
- ANOVA
7. P-value
P-value helps decide whether to accept or reject a hypothesis.
8. Confidence Intervals
Indicates the range in which a true value lies with a certain confidence (e.g., 95%).
Conclusion
Statistics is essential for understanding data, validating models, and making informed decisions in Data Science.
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