Portfolio Project: Prison Break (Python)
- Last updated on February 28, 2025 at 1:19 PM
About this Webinar
In this Dataquest Project Lab, Anna Strahl walks through a Python project analyzing helicopter prison break attempts using real-world data from Wikipedia. This session is beginner-friendly but includes insights for intermediate learners, covering data cleaning, exploratory analysis, and comparisons of different Python data structures.
Key Takeaways:
- Working with Live Data – Import and analyze data from Wikipedia using Python.
- Data Cleaning & Preprocessing – Remove unnecessary columns, extract key information (like year), and structure data for analysis.
- Comparing Python Data Structures – Learn the efficiency trade-offs between lists, dictionaries, and Pandas DataFrames when handling frequency counts.
- Data Visualization – Use Matplotlib to explore trends in prison break attempts by year and country.
- Real-World Insights – Discover patterns in escape attempts and discuss potential explanations for spikes in activity.
- Technical Interview Prep – Gain strategies for solving data problems using core Python—helpful for job interviews!
New to Python? Begin with our Python Basics for Data Analysis course to build the foundational skills needed for this project.
Before You Start: Pre-Instruction
To make the most of this project walkthrough, follow these preparatory steps:
1. Review the Project
Access the project and familiarize yourself with the goals and structure:
- Start the project here
2. Access the Solution Notebook:
You can view and download it here to see what we’ll be covering:
Helpful Tips
New to Markdown? We recommend learning the basics to format headers and add context to your Jupyter notebook: Markdown Guide.
For file sharing and project uploads, it is important that you create a GitHub account ahead of the webinar: Sign Up on GitHub.
Want to work offline?
1. Set Up Your Workspace
We’ll work with a .ipynb file, which can be rendered in the following tools:
Jupyter Notebook (local installation required)
Google Colab (browser-based, no installation needed)
2. Download the Resource Files
helper.py file that includes functions to work with web data, visualize it and more, so make sure to download the helper.py file if you want to work locally.
Next Steps
- Complete the Project: Go here to start this project in-browser.
- Share Your Work: Upload your completed project to GitHub and GitHub Gists. Share it in the Dataquest Community to receive valuable feedback and connect with fellow learners.
- Join the next webinar on March 13th, 12:30-1:30 PM EST: Analyze Kaggle’s data science survey results to determine the key skills and experience factors that influence data science career progression and compensation. Save your spot today!