Resources

• Canonical URL - https://not.badmath.org/ds217
• GitHub repo - https://github.com/christopherseaman/datasci_217

References

• Python for Data Analysis (rough basis for Python content)

• The Missing Semester (command line, git, data wrangling)

• The Linux Command Line book (command line in-depth)

• Markdown Guide (you can probably figure this one out)

  Archive.zip

Development Tools (free!)

• VS Code (it’s pretty good)
• Python (great docs & tutorials, too!)
• GitHub Codespaces (free IDE in a browser)
• Google Cloud Shell (practice command line anywhere)

Assignments

wait for it…

Lectures

1. 01) Python, the Command Line, and VS Code
   • Command line navigation and file operations
   • Python installation and virtual environments
   • VS Code setup and basic workflows
   • Introduction to development tools
2. 02) Control Your Versions! (ch 2-3)
   • Python syntax, variables, and data types
   • Control structures and functions basics
   • Git through VS Code/GitHub (GUI focus)
   • Project organization and collaboration
3. 03) NumPy & Virtual Environments (ch 4)
   • N-dimensional arrays and array creation
   • Array operations and universal functions
   • Boolean indexing and fancy indexing
   • Basic mathematical operations
4. 04) Pandas on Jupyter (ch 5-6)
   • Series and DataFrame creation
   • Data selection and filtering
   • Reading from common file formats (CSV, Excel)
   • Advanced file reading/writing operations
   • Handling messy and missing data