Jupyter Notebook Lab — Module 11#

Applied Integration, Testing & Reproducibility

Build a complete end-to-end analytics pipeline from scratch — combining every skill from the course into a single, coherent, reproducible workflow.

Download#

📓 Student Notebook .ipynb 🔑 Answer Key coming soon

Open the .ipynb file in Google Colab by selecting File → Upload notebook.

What You’ll Practice#

  1. Loading and validating data — ingest a CSV, check schema, handle missing values
  2. Transforming and enriching data — derived columns, business classifications, DataFrame merges
  3. Writing and running tests — use assertions to verify transformation logic
  4. Aggregating and analyzing — summary statistics by region, tier, and time period
  5. Visualizing results — build a multi-panel analytics dashboard
  6. Saving reproducible output — export results as a JSON report with metadata

Learning Objectives#

By completing this notebook you will be able to:

  • Design a multi-step analytics pipeline with clear function boundaries
  • Apply assertions to verify business logic before running on full data
  • Combine ingestion, transformation, analysis, and visualization in one reproducible workflow
  • Produce output that another analyst could reproduce from the same inputs
  • Identify which module’s concepts are applied at each step of the pipeline