Estadistica Practica Para Ciencia De Datos Y Python High Quality _verified_ [ 95% FAST ]

Here are three options for a post, tailored to different platforms (LinkedIn, Instagram/Twitter, and a Blog structure). All focus on the intersection of practical statistics, high-quality Python code, and data science.

Table of Contents

  1. Setup & Core Libraries
  2. Exploratory Data Analysis (The First Look)
  3. Measures of Central Tendency & Dispersion
  4. Probability Distributions (The Engine of Uncertainty)
  5. Statistical Inference: Confidence Intervals
  6. Hypothesis Testing (A/B Testing)
  7. Correlation & Covariance
  8. Regression Basics (Linear & Logistic)
  9. Key Assumptions & Diagnostics
  10. Best Practices & Pitfalls

2. Identificación de Outliers: El Método IQR

Los valores atípicos pueden arruinar tu modelo. Una forma estadística robusta de detectarlos es utilizando el Rango Intercuartílico (IQR). Here are three options for a post, tailored

Un dato se considera outlier si está por debajo de $Q1 - 1.5 \times IQR$ o por encima de $Q3 + 1.5 \times IQR$. Setup & Core Libraries Exploratory Data Analysis (The

Implementación práctica:

Q1 = data['salario'].quantile(0.25)
Q3 = data['salario'].quantile(0.75)
IQR = Q3 - Q1
# Definir límites
limite_inferior = Q1 - 1.5 * IQR
limite_superior = Q3 + 1.5 * IQR
# Filtrar outliers
outliers = data[(data['salario'] < limite_inferior) | (data['salario'] > limite_superior)]
print(f"Cantidad de outliers detectados: len(outliers)")

CI for a mean (large sample)

data = df['total_bill']
mean = np.mean(data)
sem = stats.sem(data)  # standard error of mean
ci = stats.t.interval(0.95, len(data)-1, loc=mean, scale=sem)
print(f"95% CI: ci")