Module Assessment

Overview

Congratulations on completing the Exploratory Data Analysis module! You've built the full EDA toolkit: summary statistics, histograms, box plots, correlation matrices, and outlier detection and treatment. This assessment brings all of those skills together in a single, realistic end-to-end EDA project on a new dataset.

Complete the assessment using this Colaboratory notebook.

Learning Objective

By the end of this assessment, you will have demonstrated your ability to:

• Apply EDA techniques to a new dataset.
• Summarize findings and identify potential anomalies.

Key terms

EDA report: A structured summary of the findings from an exploratory analysis — including data quality issues, distribution characteristics, key relationships, and hypotheses for further investigation.

Anomaly: An unexpected pattern or value in the data that warrants further investigation — could be an outlier, a suspicious cluster of missing values, an impossible category value, or an unexpected correlation.

Feature selection hypothesis: A preliminary judgment, based on EDA, about which features are likely to be useful predictors for a given modeling task.

Assessment Overview

This module assessment is a coding project completed in a Google Colaboratory notebook. You will receive a new dataset — a fictional retail store dataset with sales, product, and customer information — and perform a complete EDA, documenting your findings at each step.

Skills Assessed

Coding Assessment

Complete the project in this notebook. After completing all tasks, save your notebook to GitHub and submit the link for grading.

Task Overview

The dataset contains the following columns: order_id, customer_age, product_category, units_sold, unit_price, total_revenue, discount_pct, region, days_to_ship.

You will complete the following tasks:

1. Initial inspection — Load the dataset and display its shape, column types, and first 10 rows. Count missing values per column. Count duplicate rows. Write two sentences describing what the dataset contains.

2. Summary statistics — Run .describe() on all numeric columns. For any column where the mean and median differ by more than 20%, explain in one sentence why that gap exists and what it implies about the distribution.

3. Histograms — Plot histograms (with mean and median lines) for customer_age, total_revenue, and days_to_ship. For each, classify the distribution shape (normal, right-skewed, left-skewed, or bimodal) and write one sentence of interpretation.

4. Box plots — Create grouped box plots comparing total_revenue by region and unit_price by product_category. Identify which region has the highest median revenue and which category has the most outliers.

5. Correlation matrix — Compute and visualize the full correlation matrix as a heatmap. List the three strongest feature-to-feature correlations and explain whether each one makes intuitive sense.

6. Outlier detection — Apply the IQR method to total_revenue and days_to_ship. Print the outlier count and boundary values for each. Examine the flagged rows and classify each as a data error or a legitimate extreme value. Apply appropriate treatment (remove, cap, or keep with justification).

7. EDA summary — Write a short paragraph (4–6 sentences) summarizing the most important findings from your EDA: what the data looks like, any data quality issues you found, the strongest relationships between features, and which features you would prioritize as predictors if building a model to predict total_revenue.

Grading Rubric

Knowledge Check