An introduction to core sustainability concepts such as the Triple Bottom Line, UN Sustainable Development Goals (SDGs), and Environmental, Social, and Governance (ESG) criteria. This module highlights how companies can align their strategies with global sustainability standards.

Objectives:

• Understand sustainability frameworks (Triple Bottom Line, SDGs, ESG).
• Grasp the role of data in sustainability initiatives and decision-making.
• Learn the current trends in corporate sustainability strategies.

Key Topics & Contents:

• Sustainability Frameworks: Triple Bottom Line, SDGs, ESG Reporting.
• Sustainability Trends: Circular economy, NET ZERO, carbon neutrality, and sustainable finance.
• Role of Data: Introduction to the significance of data in sustainability analysis.

Introduce students to data analytics and machine learning tools to transform sustainability practices by analyzing large datasets, forecasting sustainability metrics, and solving real-world challenges. 

Objectives:

• Learn the fundamentals of data analysis and machine learning for sustainability.
• Master techniques for collecting, cleaning, and analyzing ESG data.
• Apply predictive models to forecast and optimize sustainability performance.

Key Topics & Contents:

• Introduction to Data Analytics for Sustainability: Overview of big data, key sources, and sustainability metrics.
• Predictive Modeling for Sustainability: Learn how to forecast sustainability outcomes, including emissions and energy consumption, using machine learning.
• Hands-on Predictive Tools: Practical sessions using Python, R, and sustainability-specific software for real-world data modeling.

Skills to analyze ESG data aimed at improving sustainability performance and decision-making in organizations.

Objectives:

• Evaluate organizational and sectoral sustainability efforts using ESG data.
• Use data analysis to anticipate and prepare for sustainability challenges and opportunities under different conditions.
• Assess the full environmental impact of products or services from creation to disposal using LCA tools.

Key Topics & Contents:

• ESG (Environmental, Social, and Governance) Data: Analyze ESG performance metrics to evaluate sustainability efforts at organizational and sectoral levels.
• Scenario Planning: Develop future-oriented strategies using data analysis to anticipate sustainability challenges and opportunities under different environmental and regulatory conditions.
• Life Cycle Assessments (LCA): Explore the full environmental impact of products or services from creation to disposal using LCA tools.

Participants will explore key sustainability metrics such as carbon footprint, energy usage, and water consumption. The program teaches how to gather and clean data from diverse sources, including IoT devices, satellite imagery, and sustainability reports.

Objectives:

• Learn key sustainability metrics and KPIs.
• Understand the sources of sustainability data (internal and external).
• Develop skills in data collection and preparation for analysis.

Key Topics & Contents:

• KPI Development: Learn to develop key performance indicators (KPIs) tailored to sustainability goals.
• Data Sources: IoT sensors, satellite data, sustainability reporting tools (GRI, CDP, .
• Data Collection & Cleaning: Techniques for handling structured and unstructured data, data wrangling.

Understand the implications of the EU Taxonomy, the Corporate Sustainability Reporting Directive (CSRD) and European Sustainability Reporting Standards (ESRS) on corporate governance and sustainable finance.

Objectives:

• Learn statistical and machine learning techniques to analyze sustainability data.
• Apply data analysis for trend forecasting and resource optimization.
• Build predictive models for sustainability strategy.

Key Topics & Contents:

• Regression & Time Series Analysis: Predicting energy consumption, emissions trends.
• Cluster Analysis: Segmenting customers based on sustainability behavior.
• Predictive Modeling: Optimizing resource use (e.g., energy, raw materials).

The module aims to merge data analytics and sustainability, preparing students to drive impactful changes within organizations by leveraging data intelligence for sustainability strategies.

Objectives:

• Learn how to extract actionable insights from complex sustainability datasets, enabling data-driven decision-making
• Understand how to apply sustainability intelligence to support corporate strategies
• Gain hands-on experience in using sustainability intelligence to assess and improve corporate performance through practical tools and techniques.

Key Topics & Contents:

• Turning Data into Insights: Learn how to extract actionable insights from complex sustainability datasets.
• Integrating Data with Corporate Strategy: Focus on how sustainability intelligence is integrated into corporate sustainability strategies, supporting goals like carbon neutrality, energy efficiency, and supply chain sustainability.
• Corporate Sustainability Assessments: Hands-on practice on how to use sustainability intelligence to assess and improve corporate performance.

Participants will learn how to assess ESG performance, understand the integration of sustainability into financial decision-making, and explore sustainable and transition finance mechanisms like blended finance, green bonds or impact investing.

Objectives:

• Learn how to assess ESG performance through data analytics.
• Explore the relationship between ESG data and financial risk management.
• Understand sustainable finance tools, schemes and mechanisms and their data-driven underpinnings.

Key Topics & Contents:

• ESG Performance Analysis: Quantitative approaches to evaluating ESG scores and benchmarks.
• ESG Data Sources & Tools: Bloomberg, Refinitiv, Sustainalytics, MSCI, and other platforms.

Covering decision frameworks such as multi-criteria decision analysis (MCDA) and optimization techniques, this module helps participants balance sustainability goals with profitability.

Objectives:

• Apply decision-making frameworks to align sustainability goals with business strategy.
• Use optimization techniques to enhance sustainability without sacrificing profitability.
• Perform scenario analysis to simulate the impact of sustainability decisions.

Key Topics & Contents:

• Multi-Criteria Decision Analysis (MCDA): Balancing environmental, social, and financial considerations.
• Optimization Models: Linear programming, resource optimization.
• Scenario Analysis: Monte Carlo simulations, scenario planning for sustainability risks.

The program explores cutting-edge sustainable business models, including the circular economy, product-as-a-service (PaaS), and data-driven carbon accounting practices.

Objectives:

• Explore innovative business models and their relationship with sustainability data.
• Apply data analytics to support circular economy practices.
• Develop insights into data-driven carbon reduction strategies.

Key Topics & Contents:

• Circular Economy Analytics: Data-driven material flow analysis and waste tracking.
• Product-as-a-Service (PaaS): Leveraging data for service-based business models.
• Carbon Accounting: Tools for measuring and reducing carbon footprints.

This module focuses on global sustainability reporting standards such as the EU’s Sustainable Finance framework (EU Taxonomy), GRI, SASB, and TCFD, and teaches participants how to automate and optimize reporting processes using data analytics.

Objectives:

• Understand global sustainability reporting standards and compliance requirements.
• Use data to automate and streamline sustainability reporting.
• Leverage AI tools for real-time compliance and reporting.

Key Topics & Contents:

• Global Reporting Standards: EU Taxonomy, GRI, SASB, TCFD, CDP.
• Data-Driven Sustainability Reporting: Automating sustainability scorecards and dashboards.
• Compliance with Emerging Regulations: CSRD, Do No Significant Harm (DNSH), SFDR, TCFD, Corporate Due Dilligence.
• AI in Reporting: Automation for sustainability report generation and compliance monitoring.

Students will work on a final project to analyze real-world ESG datasets for a selected company or industry. Students will apply the knowledge and techniques learned throughout the course to optimize sustainability performance, with results presented in a structured report.

Objectives:

• Apply data analytics techniques to solve a real-world sustainability problem.
• Integrate knowledge from various modules to create a comprehensive sustainability strategy.
• Present actionable insights and recommendations to stakeholders.

Key Topics & Contents:

• Real-World Case Study: Analyze data from a company or sector with a focus on sustainability.
• Data-Driven Insights: Use sustainability KPIs, ESG data, and predictive models to guide decision-making.
• Strategic Recommendations: Propose actionable, data-backed strategies for improving sustainability performance.