Lecture 4: Structured Data Processing (Part II)

(Last updated: Jan 27, 2026)

This lecture gives a tutorial about the Smell Pittsburgh project using a Jupyter Notebook. The learning goals can be found on this link.

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Preparation

Do the preparation for the structured data processing module.

Lecture

Below is the link to the online notebook:

• Structured Data Processing Tutorial Online Notebook

Follow the steps on the assignment page to set up the notebook.

Additional Resources

Below are the videos that students found useful in understanding more about the course materials:

• Pandas Resample
• Python Rolling Window Functions explained in 4 minutes
• How to Use Pandas Rolling - A Simple Illustrated Guide
• Continuous vs Discrete Data
• Introduction to Trees (Data Structures & Algorithms #9)

Below are websites that could be useful in understanding more about the course materials:

• A Practical Guide to Implementing a Random Forest Classifier in Python
• Loss Functions in Machine Learning Explained

Below are websites related to this tutorial:

• The Smell Pittsburgh Dataset
• The scikit-learn API
• The seaborn API
• The plotly API
• The pandas API
• The numpy API

Below are papers related to this tutorial:

• Quinlan, J. R. (1986). Induction of decision trees. Machine learning.
• Breiman, L. (2001). Random forests. Machine learning.