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Learning Roadmap

How to Become a AI Data Quality Analyst

A step-by-step, phase-based learning path from beginner to job-ready AI Data Quality Analyst. Estimated completion: 5 months across 4 phases.

4 Phases
18 Weeks Total
Medium Entry Barrier
Intermediate Difficulty
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  1. Data Quality Foundations & SQL Mastery

    4 weeks
    Goals
    • Master SQL for data profiling, aggregations, and quality checks
    • Understand core data quality dimensions: accuracy, completeness, consistency, timeliness, validity, uniqueness
    • Learn Python pandas for exploratory data analysis and basic validation
    Resources
    • Mode Analytics SQL Tutorial
    • Kaggle 'Pandas' micro-course
    • Great Expectations official documentation and tutorials
    • Book: 'Data Quality: Empowering Businesses with Analytics and AI' by Anuradha Wickramasinghe
    Milestone

    You can independently profile any dataset, identify quality issues, and write SQL/Python validation checks

  2. ML Data Pipelines & Labeling Quality

    5 weeks
    Goals
    • Understand how training data quality impacts model performance (bias, noise, distribution)
    • Learn annotation quality metrics and tools like Label Studio
    • Build data validation pipelines with Great Expectations and integrate into Airflow DAGs
    Resources
    • Andrew Ng's 'Data-Centric AI' course and competition materials
    • Label Studio documentation and hands-on tutorials
    • Great Expectations 'Getting Started' walkthrough
    • Andrew Ng's 'Designing Data-Centric AI Applications' (DeepLearning.AI)
    Milestone

    You can design end-to-end data quality pipelines that gate ML training data and measure annotation quality

  3. Generative AI & RAG Data Quality

    5 weeks
    Goals
    • Master evaluation frameworks for LLM outputs: faithfulness, relevance, hallucination detection
    • Learn RAG-specific quality metrics with RAGAS and DeepEval
    • Build automated prompt-response quality classifiers using OpenAI API
    Resources
    • RAGAS documentation and GitHub examples
    • DeepEval quickstart guides
    • LangSmith tracing and evaluation tutorials
    • Weights & Biases 'LLM Evaluation' course
    Milestone

    You can build automated quality evaluation pipelines for RAG systems and LLM applications end-to-end

  4. Production Systems, Governance & Portfolio

    4 weeks
    Goals
    • Learn data lineage, governance frameworks, and compliance requirements
    • Build production-grade quality dashboards and alerting systems
    • Create a portfolio project demonstrating end-to-end data quality pipeline for an AI application
    Resources
    • dbt documentation for data transformation and lineage
    • AWS Data Lake and GCP data governance whitepapers
    • Open-source datasets from HuggingFace for portfolio projects
    • GitHub Actions CI/CD tutorial for data pipelines
    Milestone

    You have a polished portfolio, understand enterprise data governance, and can architect quality systems for production AI

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

Automated Data Quality Dashboard for a Public Dataset

Beginner

Build a Python-based dashboard that profiles the UCI Adult Income or Kaggle Titanic dataset, automatically checking for missing values, outliers, class imbalance, and data type issues. Visualize quality metrics and generate a shareable HTML report.

~15h
Data profilingPython pandasStatistical analysis

ML Training Data Validation Pipeline with Great Expectations

Intermediate

Design a complete Great Expectations validation suite for a text classification dataset (e.g., AG News or IMDB). Integrate it into an Airflow DAG that gates model training based on quality checks. Generate data docs reports.

~30h
Great ExpectationsAirflow orchestrationData validation

Annotation Quality Audit System

Intermediate

Build a system that measures inter-annotator agreement on a labeled dataset, identifies low-agreement samples for re-review, and generates annotator performance reports. Use Cohen's kappa, Fleiss' kappa, and Krippendorff's alpha.

~25h
Annotation quality metricsLabel StudioStatistical testing

RAG Pipeline Quality Evaluation Suite

Advanced

Build an end-to-end evaluation pipeline for a RAG application using RAGAS and DeepEval. Create a test dataset with ground-truth contexts, evaluate retrieval precision/recall, measure generation faithfulness, and track quality across pipeline iterations.

~40h
RAGASDeepEvalRAG evaluation

Data Drift Detection and Alerting System

Advanced

Implement a real-time data drift monitoring system for a production ML feature pipeline. Use statistical tests (KS, PSI, chi-squared) to detect distribution shifts, build dashboards in Grafana or W&B, and set up automated Slack/email alerts.

~35h
Drift detectionStatistical testingMonitoring systems

LLM-Powered Data Quality Triage Bot

Advanced

Build a Slack/Teams bot that receives data quality issue reports, uses OpenAI API to classify severity and category, routes to the right team, and tracks resolution. Include a feedback mechanism to improve classification over time.

~30h
OpenAI APIPrompt engineeringAutomation

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