Data Science — Statistics

Descriptive statistics

import numpy as np
import pandas as pd

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print(f"Mean: {np.mean(data)}")
print(f"Median: {np.median(data)}")
print(f"Mode: {pd.Series(data).mode()[0]}")
print(f"Std: {np.std(data)}")
print(f"Variance: {np.var(data)}")

Distribution

from scipy import stats

# Normal distribution
x = np.linspace(-4, 4, 100)
y = stats.norm.pdf(x)

# Skewness
print(f"Skewness: {stats.skew(data)}")

# Kurtosis
print(f"Kurtosis: {stats.kurtosis(data)}")

Percentiles

print(f"25th percentile: {np.percentile(data, 25)}")
print(f"50th percentile: {np.percentile(data, 50)}")
print(f"75th percentile: {np.percentile(data, 75)}")

Correlation

import pandas as pd

df = pd.DataFrame({'x': [1,2,3,4,5], 'y': [2,4,5,4,5]})
corr = df.corr()
print(f"Correlation:\n{corr}")

Mini Practice

1. Calculate descriptive statistics
2. Analyze distributions
3. Compute correlations
4. Identify outliers

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Continue with Probability - Probability theory.