📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Multi-Modal AI for ESG Sentiment Analysis, from foundations to a certified capstone project.
Domain
• Sustainability reports, climate disclosures and regulatory filings as sources
• Voluntary reporting bias: firms disclose what flatters them
• Rating agency divergence and why two ESG scores disagree on the same firm
Extraction
• PDF parsing, table extraction and document segmentation
• Entity resolution across subsidiaries, tickers and renamed companies
• Building a labelled dataset and writing annotation guidelines that hold up
Modelling
• Domain-adapted transformers and why general sentiment models fail on ESG text
• Stance and claim detection as more useful than polarity
• Aspect-level analysis separating environmental, social and governance signals
Greenwashing
• Comparing narrative commitment against reported quantitative performance
• Vagueness, hedging and commitment-without-target language as features
• The limits of inference: a linguistic signal is not proof of intent
Fusion
• Joining textual signals to emissions and operational indicators
• Fusion strategies and the scale mismatch between modalities
• Backtesting an ESG score and reporting its limitations honestly
Earn government-registered certification in Multi-Modal AI for ESG Sentiment Analysis
e-Certificate and e-Marksheet issued on successful completion.
