Programme Parameters
Graduate / Intermediate
4 Weeks (40 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
Smart Cities and Sustainability Metrics: From Sensors to Indicators focuses on measuring what matters in a sustainable city. You learn how urban sensor networks and data sources feed sustainability indicators — energy, emissions, air, water, mobility and waste — and how to design, compute and interpret those metrics to track progress and guide policy. The course centres on the data-to-indicator pipeline that makes sustainability measurable and actionable. You finish able to reason about a smart-city sustainability-metrics system. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers smart-city sustainability metrics — turning sensor and urban data into the indicators that measure and guide a city’s sustainability.
📋 Course Objectives
1. Map urban sensor and data sources.
2. Design sustainability indicators.
3. Compute energy, emissions and mobility metrics.
4. Interpret indicators to guide policy.
5. Build a data-to-metrics pipeline.
👥 Who Should Enroll?
• Smart-city and urban professionals
• Sustainability analysts
• Data and IoT teams in cities
• Students of urban sustainability
🚀 Key Learning Outcomes
• An understanding of smart-city metrics.
• A measurement-and-indicator perspective.
• An urban-sustainability project.
• 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
AI Fundamentals, Mathematics, and Smart Cities Foundations
Apply linear algebra and calculus concepts to optimize smart city infrastructure • Develop probabilistic models to analyze sensor data and predict urban trends • Design machine learning pipelines to integrate with existing city management systems
Data Engineering, Preprocessing, and Feature Pipelines
Configure data ingestion pipelines to handle large-scale sensor data from various sources • Implement data preprocessing techniques to handle missing values and outliers in urban datasets • Evaluate feature extraction methods to improve model performance in smart city applications
Model Architecture, Algorithm Design, and Smart Cities Methods
Design convolutional neural networks to analyze satellite images for urban planning • Develop reinforcement learning algorithms to optimize traffic flow and reduce congestion • Analyze the performance of different machine learning models on various smart city datasets
Training, Hyperparameter Optimization, and Evaluation
Train deep learning models using transfer learning and fine-tuning techniques for smart city applications • Implement hyperparameter tuning using grid search and random search methods • Evaluate model performance using metrics such as accuracy, precision, and recall for urban datasets
Deployment, MLOps, and Production Workflows
Deploy machine learning models using cloud-based services such as AWS SageMaker or Google Cloud AI Platform • Configure model serving pipelines to handle real-time inference and updates • Develop monitoring and logging systems to track model performance in production environments
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze bias in machine learning models and develop strategies to mitigate its effects • Develop fairness metrics to evaluate model performance across different demographic groups • Implement transparency and explainability techniques to improve model interpretability
Industry Integration, Business Applications, and Case Studies
Develop business cases for smart city projects using machine learning and data analytics • Analyze industry trends and market demand for smart city solutions • Evaluate the return on investment (ROI) of implementing machine learning models in urban environments
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | scikit-learn |
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