Data Science Guide

Statistical modeling, experimentation, and advanced analytics.

When to Use

• Designing A/B tests and experiments

• Building predictive models

• Performing causal analysis

• Feature engineering

• Statistical hypothesis testing

Tech Stack

Category Tools

Languages Python, SQL, R

Analysis NumPy, Pandas, SciPy

ML Scikit-learn, XGBoost, LightGBM

Visualization Matplotlib, Seaborn, Plotly

Statistics Statsmodels, PyMC

Notebooks Jupyter, VS Code

Experiment Design

A/B Test Framework

import scipy.stats as stats import numpy as np

def calculate_sample_size(baseline_rate, mde, alpha=0.05, power=0.8): """Calculate required sample size for A/B test.""" effect_size = mde / np.sqrt(baseline_rate * (1 - baseline_rate)) analysis = stats.TTestIndPower() return int(analysis.solve_power( effect_size=effect_size, alpha=alpha, power=power, alternative='two-sided' ))

Example: 5% baseline, 10% relative lift

n = calculate_sample_size(0.05, 0.005) print(f"Required sample size per group: {n}")

Statistical Significance

def analyze_ab_test(control, treatment): """Analyze A/B test results.""" # Two-proportion z-test n1, n2 = len(control), len(treatment) p1, p2 = control.mean(), treatment.mean() p_pool = (control.sum() + treatment.sum()) / (n1 + n2)

se = np.sqrt(p_pool * (1 - p_pool) * (1/n1 + 1/n2))
z = (p2 - p1) / se
p_value = 2 * (1 - stats.norm.cdf(abs(z)))

return {
    'control_rate': p1,
    'treatment_rate': p2,
    'lift': (p2 - p1) / p1,
    'p_value': p_value,
    'significant': p_value < 0.05
}

Feature Engineering

Common Patterns

import pandas as pd from sklearn.preprocessing import StandardScaler

def engineer_features(df): """Feature engineering pipeline.""" # Temporal features df['hour'] = df['timestamp'].dt.hour df['day_of_week'] = df['timestamp'].dt.dayofweek df['is_weekend'] = df['day_of_week'].isin([5, 6])

# Aggregations
df['user_avg_spend'] = df.groupby('user_id')['amount'].transform('mean')
df['user_transaction_count'] = df.groupby('user_id')['amount'].transform('count')

# Ratios
df['spend_vs_avg'] = df['amount'] / df['user_avg_spend']

return df

Feature Selection

from sklearn.feature_selection import mutual_info_classif

def select_features(X, y, k=10): """Select top k features by mutual information.""" mi_scores = mutual_info_classif(X, y) top_k = np.argsort(mi_scores)[-k:] return X.columns[top_k].tolist()

Model Evaluation

Cross-Validation

from sklearn.model_selection import cross_val_score, StratifiedKFold

def evaluate_model(model, X, y): """Robust model evaluation.""" cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

scores = {
    'accuracy': cross_val_score(model, X, y, cv=cv, scoring='accuracy'),
    'precision': cross_val_score(model, X, y, cv=cv, scoring='precision'),
    'recall': cross_val_score(model, X, y, cv=cv, scoring='recall'),
    'auc': cross_val_score(model, X, y, cv=cv, scoring='roc_auc')
}

return {k: f"{v.mean():.3f} (+/- {v.std()*2:.3f})" for k, v in scores.items()}

Causal Inference

Propensity Score Matching

from sklearn.linear_model import LogisticRegression from sklearn.neighbors import NearestNeighbors

def propensity_matching(df, treatment_col, features): """Match treatment and control using propensity scores.""" # Estimate propensity scores ps_model = LogisticRegression() ps_model.fit(df[features], df[treatment_col]) df['propensity'] = ps_model.predict_proba(df[features])[:, 1]

# Match nearest neighbors
treated = df[df[treatment_col] == 1]
control = df[df[treatment_col] == 0]

nn = NearestNeighbors(n_neighbors=1)
nn.fit(control[['propensity']])
distances, indices = nn.kneighbors(treated[['propensity']])

return treated, control.iloc[indices.flatten()]

Best Practices

Analysis Workflow

• Define hypothesis clearly

• Calculate required sample size

• Design experiment (randomization)

• Collect data with quality checks

• Analyze with appropriate tests

• Report with confidence intervals

Common Pitfalls

• Multiple comparisons without correction

• Peeking at results before sample size reached

• Simpson's paradox in aggregations

• Survivorship bias in cohort analysis

• Correlation vs causation confusion