Skills application

Instruction and application

Complete

Designing an ML data strategy

In this skills application, you will put into practice the concepts from this unit by analysing a real-world scenario and designing a data strategy that is compliant, transparent, and fair.

Success criteria

To successfully complete this skills application, you must:

• Identify relevant regulations and ethical risks associated with student data.
• Apply an appropriate governance framework (e.g., AREA or SAFE-D).
• Propose an actionable data strategy (minimisation, tracking, retention).
• Recommend practical data quality and fairness checks.

Context

You are part of a cross-functional data team at a public sector agency developing a machine learning model to predictstudent performance outcomes. The model is trained on a combination of behavioural, demographic, and academic data sourced from multiple school districts.

Some of the data is sensitive, including ethnicity, socio-economic status, and disciplinary records. Education advocates have raised concerns regarding data retention, fairness, and transparency. Your task is to evaluate these concerns and design a compliant and ethical data strategy.

Instructions

Follow the prompts in the form below to complete your analysis. Completing this activity will “unlock” the solution example on the following page.

Exercise: Student Data Strategy

1. Regulatory and ethical analysis: Identify one data privacy regulation relevant to this student data context and explain its impact.

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2. Governance framework application: Select either AREA or SAFE-D. Describe how two of its principles would guide responsible data use in this project.

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3. Compliant data strategy: Propose three actions that support data minimisation, metadata tracking, and retention limits.

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4. Data quality and fairness plan: Recommend two quality control checks and one fairness check to embed in the pipeline.

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