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RDSOs

An Responsible Data Science Opportunities (RDSOs) is the core learning unit in the ConCReTE Curriculum that prompts students to think critically about the contextual dimensions of real-world scenarios. Unlike technical data science tasks, RDSOs challenge learners to engage with decision-making—before, during, or after data use—by weighing stakeholder needs, organizational values, and potential trade-offs.

Each RDSO is designed to:

  • Build digital leadership attributes like agency, confidence, and accountability.
  • Highlight decision points that reveal multiple valid paths, not just a single solution.
  • Blend principles with performance, showing that responsible data science requires both technical outputs and human-centered insight.
  • Connect directly to business objectives within a scenario’s context, reinforcing relevance to real-world roles. Used alongside data science tasks, RDSOs ensure that learners practice applying responsible data science—not just mastering tools.

Read more about digital leadership attribute, learning objectives, and skill development here.

Explore RDSOs

  • PGH

  • PGH.001 PGH.001

    • Data Quality

    Question

    What is the best way to extract data from a PDF?

    Learning Goal

    Comparing and evaluating data quality

  • PGH.002 PGH.002

    • Transparency
    • Privacy
    • Data Quality
    • Security

    Question

    Is AI currently a viable tool for data extraction from a technical standpoint? From a security and privacy standpoint?

    Learning Goal

    Evaluating trust in AI from multiple perspectives

  • PGH.003 PGH.003

    • Transparency
    • Accessibility
    • Impact Assessment

    Question

    How much of model building are you comfortable with being abstracted from you? Do you want to see things you don’t understand?

    Learning Goal

    Weighing black-box model skepticism with realistic assessment of knowledge

  • Parcels

  • Parcels.001 Parcels.001

    • Fairness
    • Transparency
    • Privacy
    • Ethics
    • Context Awareness
    • Accessibility

    Question

    Does visualizing parcels on a map facilitate responsible planning and stakeholder perspective?

    Learning Goal

    Considering how delivery of data affects response

  • Parcels.002 Parcels.002

    • Fairness
    • Transparency
    • Ethics
    • Context Awareness
    • Beneficence

    Question

    Is eligibility for consideration determined in an responsible manner? How or how not?

    Learning Goal

    Critically evaluating ethics of methodology

  • Parcels.003 Parcels.003

    • Fairness
    • Ethics
    • Data Quality

    Question

    Should the location of the parcels impact the selection process? Why or why not?

    Learning Goal

    Integrating multiple parameters into decision making

  • Parcels.004 Parcels.004

    • Transparency
    • Ethics
    • Context Awareness
    • Beneficence

    Question

    Walk through your decision-making process for identifying the top five parcels. What questions or concerns does your approach raise?

    Learning Goal

    Introducing intentionality into the decision-making process

  • Student Success

  • SSS.001 SSS.001

    • Beneficence
    • Criticality

    Question

    Can you explain the rationale behind your variable selections?

    Learning Goal

    Introducing intentionality into the decision-making process

  • SSS.002 SSS.002

    • Transparency
    • Impact Assessment

    Question

    How will you balance organizational goals with ethical data use?

    Learning Goal

    Balancing efficacy with responsibility