Skip Navigation

Checklist for Building Data Science Education

The Department's model for the implementation—and continuous improvement—of evidence-based interventions, including those related to data education and data literacy, includes five steps.

The following checklist summarizes the key components of each step.

  1. Identify Local Needs
    • Consult existing learning-based requirements and student needs
    • Meaningfully engage local stakeholders
    • Seek systematic alignment with post-graduation pathways
    • Consider piloting first, and design for iterative goals
    • Determine outcomes that matter most to your context and locality
  2. Select Relevant, Evidence-Based Interventions
    • Select programs that demonstrate a clear rationale and support infrastructure
    • Select programs that reflect components of evidence-based practices
    • Explore prior data science programs and outcomes from peer schools and districts
  3. Plan for Implementation
    • Build a logic model for your approach
    • Develop detailed implementation plans, timelines and workplans
    • Ensure sufficient time and resources for educator training
    • Ensure sufficient resources for acquiring hardware and software
    • Ensure sufficient time for student and family engagement
    • Develop plans for continuous improvement, data collection and evaluation
  4. Implement
    • Implement your plans
    • Collect data
  5. Examine and Reflect
    • Review data and refine your approach
    • Begin continuous improvement cycle again as appropriate