A practical workflow for a technical project#

This workflow is designed for computer science and data driven projects that run in a semester.

Phase 1: Define#

• Research question and motivation
• Short list of related work
• Feasibility check and scope limits

Phase 2: Design#

• Data plan and access method
• Baseline or comparison approach
• Evaluation metrics and success criteria

Phase 3: Build and test#

• Small prototype or pilot study
• Iterative experiments with clear logs
• Version control for code and data

Phase 4: Analyze#

• Clean the data and track decisions
• Run the analysis more than once
• Validate results with simple checks

Phase 5: Communicate#

• Write a focused abstract first
• Use figures that show comparisons
• Document limitations and next steps

Reproducibility tips#

• Use a README for setup and commands.
• Pin package versions when possible.
• Keep raw data separate from processed data.
• Save experiment settings and parameters.