Scenarios
In the ConCReTE (Context-Centered Responsible Data Science Training and Exploration) Curriculum, a Scenario is a structured, industry-specific learning pathway built off a CONTEXT + DATASET (C+DS) and made up of multiple Responsible Data Science Opportunities (RDSOs). Together, these RDSOs form a coherent, real-world story that places learners in authentic professional contexts where they must make decisions, weigh trade-offs, and respond to evolving challenges.
Rather than teaching skills in isolation, scenarios simulate how data science and AI are actually used in practice—within complex organizational, social, and ethical environments. Each scenario unfolds over time, requiring learners to apply technical, strategic, and human-centered judgment as new information, constraints, and stakeholder needs emerge.
Scenarios are designed to:
- Ground learning in realistic industry contexts (e.g., healthcare, finance, civic systems, retail).
- Connect individual RDSOs into a meaningful narrative arc.
- Support personalization based on a learner’s goals, background, and pace.
- Build toward digital leadership attributes: agency, confidence, and accountability.
As learners progress through a scenario, they are not just solving problems—they are practicing what it means to lead with data and AI: selecting tools, evaluating risks, collaborating with others, identifying harms, and communicating decisions responsibly. Scenarios integrate online simulations with experiential learning, community engagement, and real-world datasets, creating a bridge between theory and practice.
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Student Success
In this scenario, learners complete a series of RDSOs centered on responsible variable selection for a student success early warning system. Rather than building a model, they must evaluate potential data inputs for privacy, fairness, compliance, and real-world impact, culminating in a Variable Selection Brief that governs what data can—and cannot—be used. The focus is on practicing judgment, not just technical skill.
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Parcels
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PGH
In this scenario, learners engage in a series of RDSOs focused on responsible data extraction, validation, and forecasting for the City of Pittsburgh’s revenue strategy. Stepping into the role of a data team supporting the Chief Data Officer, they must evaluate multiple human and AI-assisted methods for extracting financial data from public reports, assessing each for accuracy, bias, transparency, and reliability. Rather than jumping to predictions, learners compare methodologies, identify sources of error, and determine which approaches are trustworthy enough to inform long-term planning. The scenario emphasizes that responsible public-sector analytics depends on careful data handling, methodological accountability, and clear documentation before any modeling or forecasting begins.