Next Steps

Congratulations on completing the practical! Here are some useful resources.

Stack Overflow & Forums

These communities are invaluable:

For R

For Python

Statistics with R/Python:

Build Your Portfolio

Project Ideas

  1. Analyze local transport patterns using open transport data
  2. Recreate published analyses to learn techniques
  3. Contribute to open source R or Python packages

Share Your Work

  • Create a GitHub profile for your code
  • Build a portfolio website with Quarto
  • Write blog posts about what you learn
  • Share projects on social media (#RStats, #DataScience)

Advanced Topics to Explore

Once you’re comfortable with basics:

Data Science Skills

  • Version Control: Git and GitHub for collaboration
  • Reproducible Research: R Markdown, Quarto, Jupyter notebooks

Specialized Topics

  • Spatial Data Science: Working with spatial data (check out geocompx.org)
  • Machine Learning: Supervised and unsupervised learning
  • Deep Learning: Neural networks for complex patterns
  • Time Series Analysis: For temporal data
  • Network Analysis: For transport networks and relationships

Final Tips

TipThe Best Way to Learn

Practice, practice, practice! Learning data science is like learning a language—you need to use it regularly to improve.

ImportantDon’t Get Overwhelmed

There’s a lot to learn, but you don’t need to learn everything at once. Pick one area, get comfortable, then expand gradually.

NoteStay Curious

The field of data science is constantly evolving. Stay curious, keep learning, and don’t be afraid to experiment!

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