📚 Syllabus & Course Curriculum
Environmental Science & Sustainability
Module-by-module breakdown of AI-Driven Digital Twins for Battery Life Cycle Assessment, from foundations to a certified capstone project.
Outline
Clean and preprocess real‑world battery cycling data • Map time‑series data to dynamic LCA parameters • Configure Python environment and essential libraries
Outline
Build predictive models with XGBoost and Random Forest • Tune hyper‑parameters and evaluate model performance • Forecast remaining useful life and carbon‑footprint impact
Outline
Deploy a Streamlit dashboard integrating the AI models • Create dynamic visualizations with Plotly for real‑time scenario analysis • Generate actionable LCA impact reports for research or grant proposals
Earn government-registered certification in AI-Driven Digital Twins for Battery Life Cycle Assessment
e-Certificate and e-Marksheet issued on successful completion.
