Data analysis

Data Analysis Patterns

Analysis Workflow

The Analysis Pipeline

1. Define Question — what decision does this answer?
2. Collect Data — identify sources, assess quality
3. Clean Data — handle nulls, outliers, types
4. Explore (EDA) — distributions, correlations, anomalies
5. Analyze — apply appropriate statistical method
6. Visualize — make findings legible
7. Interpret — connect back to business question
8. Communicate — executive summary + supporting detail

Descriptive Statistics

Key Metrics

Metric	When to Use	Python
Mean	Normal distribution, no outliers	df.mean()
Median	Skewed data, outliers present	df.median()
Mode	Categorical, most common value	df.mode()
Std Dev	Spread of normal distribution	df.std()
IQR	Spread when outliers present	df.quantile(0.75) - df.quantile(0.25)
Percentiles	Distribution shape	df.quantile([.25,.5,.75,.95,.99])

EDA Checklist

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv('data.csv')

# Shape and types
print(df.shape)
print(df.dtypes)
print(df.head())

# Missing values
print(df.isnull().sum() / len(df) * 100)  # % missing

# Distributions
df.describe()  # count, mean, std, min, quartiles, max

# Correlations
df.corr()

# Categorical counts
df['category_col'].value_counts()

# Outliers (IQR method)
Q1 = df['value'].quantile(0.25)
Q3 = df['value'].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df['value'] < Q1 - 1.5*IQR) | (df['value'] > Q3 + 1.5*IQR)]

Hypothesis Testing

Choosing the Right Test

Question type                          → Test
─────────────────────────────────────────────────────
Compare means, 2 groups, normal        → t-test (independent)
Compare means, 2 groups, paired        → paired t-test
Compare means, 3+ groups               → ANOVA
Compare proportions, 2 groups          → z-test for proportions
Association between 2 categorical      → Chi-square test
Correlation, continuous                → Pearson (normal) / Spearman (non-normal)
Non-normal, 2 groups                   → Mann-Whitney U
Non-normal, 3+ groups                  → Kruskal-Wallis

Hypothesis Testing Framework

from scipy import stats

# 1. State hypotheses
# H0: no difference (null)
# H1: there is a difference (alternative)

# 2. Set significance level
alpha = 0.05  # 5% false positive rate

# 3. Run test
# Two-sample t-test
t_stat, p_value = stats.ttest_ind(group_a, group_b)

# 4. Interpret
if p_value < alpha:
    print(f"Reject H0: p={p_value:.4f} < alpha={alpha}")
    print("Statistically significant difference found")
else:
    print(f"Fail to reject H0: p={p_value:.4f} >= alpha={alpha}")
    print("No statistically significant difference")

# 5. Effect size (practical significance)
cohens_d = (group_a.mean() - group_b.mean()) / 
           ((group_a.std()**2 + group_b.std()**2) / 2)**0.5
# d < 0.2: small, 0.2-0.8: medium, > 0.8: large

A/B Test Analysis

Pre-Test Planning

from statsmodels.stats.power import NormalIndPower

# Calculate required sample size
analysis = NormalIndPower()
n = analysis.solve_power(
    effect_size=0.1,    # minimum detectable effect (10% relative lift)
    alpha=0.05,         # significance level
    power=0.8,          # 80% power (chance of detecting real effect)
    alternative='two-sided'
)
print(f"Required n per group: {n:.0f}")

A/B Test Results Template

Experiment: [Name]
Hypothesis: [Changing X will increase Y by Z%]
Start: [Date]  End: [Date]
Traffic split: 50/50

Results:
                Control    Treatment   Δ        p-value
Conversion rate  3.2%       3.8%      +18.75%   0.031
Revenue/visitor  $1.24      $1.41     +13.7%    0.047
Sample size      12,400     12,380    —         —

Statistical significance: YES (p < 0.05)
Practical significance: YES (18.75% lift exceeds 10% MDE)
Recommendation: SHIP treatment

A/B Test Pitfalls

• Peeking problem — don't stop early when you see significance; use sequential testing
• Multiple testing — each additional metric tested inflates false positives; use Bonferroni correction
• Novelty effect — new features get spike in engagement; run for 2+ weeks
• Sample ratio mismatch — if actual split differs from intended (e.g., 52/48 instead of 50/50), investigate before analyzing
• Segment imbalance — ensure groups are balanced on key covariates

Regression Analysis

Linear Regression

import statsmodels.api as sm

X = df[['feature1', 'feature2', 'feature3']]
y = df['target']

X = sm.add_constant(X)
model = sm.OLS(y, X).fit()

print(model.summary())
# Key outputs:
# R-squared: % variance explained
# Coef: effect of each feature on target
# p-value: significance of each feature
# Conf Int: uncertainty range

Logistic Regression (binary outcome)

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, roc_auc_score

model = LogisticRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))
print(f"AUC-ROC: {roc_auc_score(y_test, model.predict_proba(X_test)[:,1]):.3f}")

Data Visualization Principles

Chart Selection Guide

Data type	Chart type
Distribution	Histogram, box plot, violin plot
Trend over time	Line chart
Part-to-whole	Pie (≤5 slices), stacked bar, treemap
Comparison	Bar chart (horizontal for long labels)
Correlation	Scatter plot, heatmap
Composition	Stacked area, waterfall
Geographic	Choropleth, bubble map

Design Principles

• One message per chart — write the insight as the chart title
• Remove chartjunk — eliminate gridlines, borders, 3D effects
• Label directly — avoid legends that require eye movement
• Color sparingly — highlight only what matters; gray everything else
• Sort meaningfully — bar charts by value (not alphabetically) unless category order matters

import matplotlib.pyplot as plt
import seaborn as sns

# Clean chart template
fig, ax = plt.subplots(figsize=(10, 6))
sns.set_style("whitegrid")

# Bar chart with direct labels
bars = ax.barh(categories, values, color=['#2196F3' if v == max(values) else '#BDBDBD' for v in values])
ax.set_title("Conversion Rate by Channel", fontsize=14, fontweight='bold', loc='left')
ax.set_xlabel("Conversion Rate (%)")
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Direct labels
for bar, val in zip(bars, values):
    ax.text(bar.get_width() + 0.1, bar.get_y() + bar.get_height()/2, 
            f'{val:.1f}%', va='center')

Communicating Findings

Insight Communication Framework (SCQA)

• Situation — what is the context?
• Complication — what changed or is the problem?
• Question — what question does this raise?
• Answer — your finding and recommendation

Executive Summary Formula

Finding: [X happened / X is true]
So what: [This means Y for the business]
Action: [We should do Z because of this]
Evidence: [p-value / % lift / confidence interval]