IAP Bootcamp

• Data analysis with Python
  • Why do data analysis with Python?
  • Introduction: the research problem
  • Good file layout practices
  • Oh yaml, my yaml
  • Loading flow data
  • Introduction to Pandas
  • Plotting with Seaborn
• Day 0: Software and training setup
  • EHS setup and trainings
  • Software and webservices
• Computational environment check
  • Initial environment check
  • Protocols check
  • Completion date
• An introduction to Git
  • Acknowledgements
  • Motivation
  • Git by example
  • Basic Git terms
  • Downloading or starting a repository
  • Basic history terms and git status: What’s happening?
  • Branching out, adding, and checking out files
  • Snapshotting your work
  • Ignoring files
  • Recovering history: reverting commits and file-level checkouts
  • Working with others: remotes and inevitable merge conflicts
  • Conclusion
• Software Tips and Tricks
  • One date format to rule them all/naming
  • Paper RSS feeds
  • Creating feeds from searches
  • Zotero
  • Better Quartzy
  • Regex help
  • Fonts
  • Activate SnapGene remotely
• Startup checklist when working with repositories
  • New repository (Python)
  • New repository (Julia)
  • New repository (R)
  • Existing, non-data-driven repository (e.g. protocols)
  • Existing, data-driven repository (e.g. tangles_model)
• On Terminals and Shells
  • Motivation
  • The basics
  • Interface basics
  • Starting off at home
  • Moving away from home
  • Relative and absolute paths
  • File operations
  • Editing and viewing files
  • Finding things in documents
  • Exercise
  • Extras

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