The capture the flag (CTF) challenges require performing operations on data. You can use your preferred data analysis environment (including spreadsheets) to complete the challenges. If you don’t have a preferred environment, we encourage the following:
- Google Sheets or Microsoft Excel
- If you’re comfortable with Python you can use Python with the pandas and matplotlib libraries in a Jupyter notebook.
The CTF is about answering questions with data.
Your task is to become somewhat familiar with a data analysis environment. That way you can get the most out of the CTF. We’d like to avoid situations where you spend half the time setting up your environment and looking up how to read in a CSV. There's no shame in using Excel or Google Sheets for the CTF.
If you’re already familiar with an environment, feel free to explore the datasets! If not, we have tutorials in the "Tutorials" folder for Jupyter Notebook, Excel, and Google sheets. Please go through one of these. The tutorials should take 30min or less.
In the "Tutorials" folder open the PDF named Excel_tutorial.pdf. Follow the tutorial in that file. Don’t be intimidated by the number of pages! It’s mostly filled with screenshots.
In the "Tutorials" folder open the PDF named Google_sheets_tutorial.pdf. Follow the tutorial in that file. Don’t be intimidated by the number of pages! It’s mostly filled with screenshots.
Go to the "Tutorials/jupyter_notebook_tutorial" directory. It contains a dataset and a Jupyter notebook that’ll guide you through exploring the data. Download the file to your local machine.
To use a Jupyter notebook follow the instructions to install a “classic Jupyter Notebook” here.
The only Python requirements you should need are the pandas and matplotlib libraries. To avoid messing with your current python dependencies, you can install the libraries and launch Jupyter notebook in a virtual environment or conda environment.
Launch Jupyter notebook, open jupyter_notebook_tutorial.ipynb with Jupyter and follow along!
After going through the tutorial, you might find the Pandas cheatsheet in the folder useful.