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AI Data Analysis

From data foundations through data wrangling to visualization and storytelling: build a trustworthy, repeatable AI-assisted data-analysis workflow.

13 courses · Estimated effort: 123 hours

Who this is for

  • Staff who regularly clean, analyze, and present data
  • Anyone who wants AI to speed up data analysis while staying accountable for the result

Outcomes

  • Build data literacy and foundational ESG/data-science concepts
  • Use AI to help identify and handle missing values, duplicates, and anomalies
  • Turn analysis results into a clear, verifiable data story
  • Lead a real industry project’s proof of value and pilot with data and evidence

Competencies developed

  • Data Analysis
  • AI Application & Use-Case Design
  • Question Framing & Evidence Boundary
  • Data Inspection
  • Types & Units
  • Missing Values & Duplicates
  • Aggregation
  • Derived Metrics
  • Visualization
  • Verification
  • AI Code Review
  • Reproducibility & Provenance
  • Schema & Grain
  • Type, String & Date Quality
  • Missing & Duplicate Decisions
  • Reshape Correctness
  • Join Cardinality
  • Units & Definitions
  • Validation & Data Contract
  • Lineage & Cleaning Log
  • Chart Type Selection
  • Distribution & Relationship Reading
  • Scale, Baseline & Denominator Integrity
  • Accessible Encoding & Direct Labeling
  • Bounded Claims & KPI Aggregation
  • Misleading-Chart & AI-Chart Review
  • Distribution & Robust Summary Judgment
  • Outlier Investigation & Sampling Limits
  • Target Population & Association Framing
  • Confounding & Interval-Estimate Intuition
  • Data Quality vs. Statistical/Practical Significance
  • EDA Rigor & AI-Assisted Review
  • Digital Finance Literacy
  • Data-Source Selection & Fitness
  • Evidence vs Inference Reasoning
  • AI Output Verification
  • Sustainable-Finance Decision Judgment
  • Prediction Problem Framing
  • Baseline Comparison
  • Split Integrity & Leakage Detection
  • Regression Evaluation (MAE/RMSE)
  • Classification, Precision/Recall & Threshold
  • Overfitting, Cross-Validation & Tuning
  • Feature Importance & Fairness
  • Error Analysis, Drift & Deployment Judgment
  • AI Role Boundaries in Data Science
  • Data Context: Schema, Grain & Units
  • Verifiable Prompt Design
  • Read-Before-Run Code Review
  • Silent Analytical Error Debugging
  • Verification Ladder (Data → Code → Method → Claim)
  • Prompt/Code Provenance & Reproducibility
  • Sensitive Data & AI Governance
  • Workflow Ownership & Human Approval
  • Data Product Framing
  • Function & Stage Decomposition
  • Configuration & Secret Separation
  • Data Contract & Validation
  • Reliable Scheduling & Idempotency
  • Observability & Lineage
  • Approval & Rollback Governance
  • Testing & Failure Recovery
  • Analytics Workflow Canvas Design
  • Agent-vs-Workflow Justification
  • Intent-to-Analysis-Plan Translation
  • Tool Contract Design (Allowlisted, Read/Write-Separated)
  • Semantic Layer & Trusted Metrics
  • Permission Propagation & Data Minimization
  • Verification Harness Before Answering
  • Golden-Question Evaluation & Prompt-Injection Defense
  • Observability, Audit, and Failure Recovery
  • Governed Data Agent Delivery (Architecture, Prototype, Evaluation Pack)
  • Industry AI Project Charter Design
  • Industry AI PoV Prototype & Evaluation Design
  • Enterprise AI Readiness Review
  • Enterprise AI Pilot Execution & Evaluation

Required before starting

None

  • AI 101 · Claude AI Workforce Fundamentals
  • AD 101 · AI Use Case Discovery

Course sequence

  1. 1.DS 101 ESG Data Science Foundations with Python & AI
    Not startedView course
  2. 2.DS 102 Data Wrangling & Data Quality
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  3. 3.DS 103 Data Visualization & Data Storytelling
    Not startedView course
  4. 4.DS 104 Exploratory Data Analysis & Statistical Thinking
    Not startedView course
  5. 5.DS 105 Web 3.0 and Big Data Analysis in Fintech, Green and Sustainable Finance
    Not startedView course
  6. 6.DS 201 Predictive Analytics & Machine Learning Foundations
    Not startedView course
  7. 7.DS 202 AI-Assisted Data Science
    Not startedView course
  8. 8.DS 301 Data Products & Analytics Workflow Engineering
    Not startedView course
  9. 9.DS 302 Data Agents & Intelligent Analytics Systems
    Not startedView course
  10. 10.IL 101 IL 101 | Industry AI Project & Value Design
    Not startedView course
  11. 11.IL 201 IL 201 | Industry AI Proof-of-Value Co-creation Lab
    Not startedView course
  12. 12.IL 202 IL 202 | Enterprise AI Data, Governance & Integration Readiness
    Not startedView course
  13. 13.IL 301 IL 301 | Enterprise AI Pilot & Adoption Lab
    Not startedView course

Next step

DS 101 · ESG Data Science Foundations with Python & AI

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