Introduction to the Course

Smart Cities and Sustainability Metrics: From Sensors to Decisions is a 3-week online course designed to teach how AI, IoT, and data analytics can be applied to monitor, measure, and optimize urban sustainability. Students will explore sensor networks, data-driven decision-making, and sustainability metrics, gaining practical skills to transform urban planning and resource management.

Course Objectives

By the end of the course, participants will be able to:

• Connect raw sensor data to real urban decisions

• Understand sustainability metrics and reporting in the context of city systems

• Improve data quality practices for reliable city monitoring

• Build and interpret dashboards for tracking sustainability KPIs

• Collaborate effectively across technology, planning, and policy teams

What Will You Learn (Modules)

Module 1: Introduction to Smart Cities & Urban Sustainability

• Overview of smart city concepts and sustainability challenges

• Key urban indicators: energy, water, mobility, and waste

• Role of IoT and AI in urban planning

Module 2: Sensor Networks & Data Collection

• Types of urban sensors and data sources

• Real-time monitoring of city infrastructure

• Data acquisition, cleaning, and storage strategies

Module 3: Sustainability Metrics & KPIs

• Designing metrics for energy, environment, mobility, and resource usage

• Quantitative and qualitative assessment frameworks

• Benchmarking and comparative analysis

Module 4: AI & Analytics for Urban Decision-Making

• Data-driven prediction and optimization models

• Machine learning for urban trend analysis and anomaly detection

• Scenario modeling for sustainable urban management

Module 5: Smart Mobility & Energy Optimization

• AI applications for traffic management and energy efficiency

• Renewable energy integration and smart grids

• Case studies of sustainable urban interventions

Module 6: Visualization & Reporting for Sustainability

• Data visualization techniques for city dashboards

• Reporting and communicating sustainability performance

• Stakeholder engagement and decision support

Module 7: Capstone Project

• Collect, analyze, and interpret real-world urban data

• Design sustainability metrics and provide actionable recommendations

• Present insights for city planners, policymakers, or organizations

Who Should Take This Course?

• Urban planners and smart city professionals
• Data scientists and AI enthusiasts interested in sustainability
• Researchers in urban studies, civil engineering, or environmental science
• Graduate students in smart city planning, sustainability, or data analytics
• Professionals working in municipal management, IoT, or infrastructure

Job Oppurtunities

After completing this course, learners will be prepared for roles such as:

• Smart City Analyst: Monitors urban metrics and recommends improvements
• Urban Data Scientist: Analyzes city data for sustainability and planning
• Sustainability Consultant: Advises organizations and cities on resource efficiency
• IoT & Sensor Network Specialist: Designs and manages urban monitoring systems
• Urban Planner & Policy Analyst: Uses data to inform sustainable city policies
• Energy & Resource Optimization Specialist: Applies AI to improve urban efficiency

Why Learn With Nanoschool ?

• Expert-led training: Learn from AI, sustainability, and urban planning specialists
• Hands-on learning: Work with real-world urban datasets, dashboards, and case studies
• Industry relevance: Stay updated with smart city technologies, IoT, and sustainability metrics
• Career-focused: Develop skills for urban analytics, planning, and smart city roles
• Flexible learning: Online self-paced modules suitable for professionals and students

Key outcomes of the course

• Gain expertise in smart city analytics and sustainability metrics
• Perform practical sensor data collection, analysis, and visualization
• Learn to optimize urban resources and improve sustainability
• Enhance career prospects in urban planning, smart cities, and sustainability
• Build confidence to implement data-driven solutions for real-world cities