Goal

Given a marketing campaign dataset, you will:

• compute descriptive statistics
• compare two segments (A vs B)
• build confidence intervals
• produce a short written conclusion

Example dataset columns

• user_iduser_id
• variantvariant (A/B)
• convertedconverted (0/1)
• revenuerevenue
• countrycountry

Step 1: Load

import pandas as pd
 
df = pd.read_csv("data/campaign.csv")
print(df.head())

import pandas as pd
 
df = pd.read_csv("data/campaign.csv")
print(df.head())

Step 2: Quick summary

summary = (
    df.groupby("variant")
      .agg(
          users=("user_id", "nunique"),
          conversion_rate=("converted", "mean"),
          avg_revenue=("revenue", "mean"),
          median_revenue=("revenue", "median"),
      )
)
print(summary)

summary = (
    df.groupby("variant")
      .agg(
          users=("user_id", "nunique"),
          conversion_rate=("converted", "mean"),
          avg_revenue=("revenue", "mean"),
          median_revenue=("revenue", "median"),
      )
)
print(summary)

Step 3: Visualize

Use either Matplotlib/Seaborn/Plotly:

• conversion bar chart
• revenue distribution comparison

Step 4: Test conversion difference (approx)

Use the “A/B Testing Basics” approach.

Step 5: Deliverable

Write a short conclusion:

• Does variant B improve conversion?
• Is the result practically meaningful?
• Any data quality concerns?

The project pipeline

  flowchart LR
  A["Load campaign.csv"] --> B["Descriptive stats
per variant"]
  B --> C["Confidence interval
on conversion rate"]
  C --> D["A/B test
(z-test or t-test)"]
  D --> E["Written conclusion"]

🧪 Try It Yourself

Exercise 1 – Descriptive summary by variant

Exercise 2 – CI on the conversion-rate difference

Exercise 3 – Writing a defensible conclusion

Next

This wraps up the Statistics phase. Carry these tools — descriptive stats, confidence intervals, and hypothesis testing — into modeling and machine learning, where the same ideas reappear as regression diagnostics and model evaluation metrics.