Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Build an end-to-end ESG AI pipeline in Python—from sustainability reports and climate disclosures to transformer-based sentiment/stance models, greenwashing risk detection, and ESG scoring, with an intro to multi-modal fusion using numeric ESG indicators. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Build an end-to-end ESG AI pipeline in Python—from sustainability reports and climate disclosures to transformer-based sentiment/stance models, greenwashing risk detection, and ESG scoring, with an intro to multi-modal fusion using numeric ESG indicators.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
ESG Data and Its Problems
• 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
Getting Text Out of Reports
• 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
Sentiment and Stance
• 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
Detecting the Gap
• 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
Combining Text with Numbers
• 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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | NLTK |
| Covered Tool / Platform | spaCy |
| Covered Tool / Platform | Hugging Face Transformers |
| Covered Tool / Platform | Gensim |
| Covered Tool / Platform | BERT |
| Covered Tool / Platform | GPT |
Frequently Asked Questions
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