Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This course delves into the synergy between Life Cycle Assessment (LCA) and Generative AI to automate and optimise impact reporting. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course delves into the synergy between Life Cycle Assessment (LCA) and Generative AI to automate and optimise impact reporting.
📋 Course Objectives
1. Apply biotechnology methods to authentic research and industry problems.
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 biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• A demonstrable biotechnology project for your research or industry portfolio.
• 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
Day 1 – The Foundation: Data, Frameworks, and APIs
Explore GRI, ISO 14040 sustainability reporting standards • Understand APIs, LLM fundamentals and security best practices • Set up Google Colab, load sample emissions data and make first API call with Python
Day 2 – The Logic: Prompt Engineering & LangChain
Craft system vs. user prompts to guide AI behaviour • Mitigate hallucinations and ground AI responses in data • Implement LangChain for robust prompt pipelines and testing
Day 3 – The Insight: Batch Processing & Document Output
Automate repetitive LCA tasks with Python loops • Structure multi‑section reports and integrate human‑in‑the‑loop review • Generate exportable Markdown reports for ESG compliance
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | LangChain |
| Covered Tool / Platform | Large Language Models (LLMs) |
| Covered Tool / Platform | APIs |
Frequently Asked Questions
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