This introductory module provides a comprehensive view of the main sustainability frameworks (Triple Bottom Line, SDGs, ESG) and key trends such as the circular economy and Net Zero. In addition, it highlights the importance of data and technology in driving sustainable strategies and generating business value in the medium- and long-term.

OBJECTIVES

• Apply sustainability frameworks and standards (TBL, SDG, ESG) in business management. 
• Analyze global trends (circular economy, Net Zero, sustainable finance) and their strategic impact.
• Understand the role of data in sustainability initiatives and decision-making.

KEY THEMES AND CONTENT

• Sustainability Frameworks: Triple Bottom Line, SDGs, ESG Reports.
• Sustainability Trends: Circular economy, Net Zero, carbon neutrality and sustainable finance.
• The role of data: Introduction to the importance of data in sustainability analysis.
• Efficiency and responsibility in technological development for sustainability.

This module offers a solid foundation in programming, statistics and machine learning, which are essential for analyzing sustainability data. In addition, it addresses visualization techniques for transforming complex data into strategic information.

OBJECTIVES

• Master programming and statistical fundamentals for data analysis.
• Apply machine learning and AI techniques to sustainability.
• Create effective visualizations to communicate ESG results.

KEY THEMES AND CONTENT

• Fundamentals of programming.
• Fundamentals of statistics.
• Fundamentals of machine learning and AI.
• Fundamentals of communication and data visualization.

This module explores the global standards in ESG reporting, compliance with emerging regulatory standards and the role of AI in automation in corporate sustainability. We will also address digital strategies for efficient ESG data management.

OBJECTIVES

• Identify and apply the main ESG reporting standards (GRI, SASB, TCFD, CSRD) for sustainable business management.
• Analyze the impact of biodiversity on corporate strategy using frameworks such as TNFD and Nature Positive approaches.
• Implement digital solutions and artificial intelligence to automate sustainability reporting and ensure ESG regulation compliance.

KEY THEMES AND CONTENT

• Global reporting standards: EU Taxonomy, GRI, SASB, TCFD, CDP.
• Biodiversity assessment: TNFD (Taskforce on Nature-related Financial Disclosures) and Nature positive.
• Compliance with emerging international and European regulation: CSRD, Do No Significant Harm (DNSH), SFDR, TCFD, Corporate Diligence.
• AI in reporting: Automation for sustainability reporting and compliance monitoring.
• Digital strategy and architecture for ESG management.

Participants will learn how to manage and optimize sustainability data using advanced tools and sources, and how to develop advanced sustainability KPIs.

OBJECTIVES

• Understand ESG data sources and platforms: Learn how to use tools such as Bloomberg, Refinitiv and MSCI to assess sustainability performance.
• Manage and purge data: Acquire skills to organize and prepare structured and unstructured data for analysis.
• Create sustainability KPIs: Learn how to develop KPIs aligned with sustainability objectives.

KEY THEMES AND CONTENT

• ESG data sources and tools: Bloomberg, Refinitiv, Sustainalytics, MSCI and other platforms.
• Alternative data sources: IoT sensors, satellite data, sustainability reports (GRI, CDP).
• Data collection and cleansing: Strategies for managing structured and unstructured data and optimizing its readiness for analysis.
• KPI development: Creation of key performance indicators adapted to sustainability objectives.

This module focuses on advanced ESG data analysis and risk management, including environmental impact assessment through LCA (life cycle assessment), using quantitative and artificial intelligence tools to optimize sustainable performance.

OBJECTIVES

• Analyze ESG data: Learn how to apply quantitative approaches to assess ESG metrics at different organizational and sectoral levels.
• Manage ESG risks: Understand how to use frameworks such as IFRS, TCFD and CSRD to assess ESG risks and adjust creditworthiness analyses to climate risks.
• Apply LCA and machine learning: Explore how life cycle assessments and the use of machine learning can mitigate the environmental impact of products and services.

KEY THEMES AND CONTENT

• ESG (Environmental, Social and Governance) data:
• Quantitative approaches to analyzing ESG performance metrics; sustainability at the organizational and sectoral level.
• ESG risk management, frameworks such as IFRS and TCFD, and portfolio analysis and CSRD. Climate risks: Creditworthiness ratings adjusted for climate risk.
• Life cycle assessments (LCA): Explore the total environmental impact of products or services—from creation to disposal—using LCA tools.
• Application of ML in the analysis and optimization of products’ and services’ LCA, and the use of AI to minimize environmental impact.

Application of artificial intelligence and machine learning to process ESG metrics, using advanced tools such as natural language processing, predictive models and scenario analysis to improve resilience and decision-making in sustainability.

OBJECTIVES

• Apply natural language processing (NLP) in the analysis of ESG data and extract key insights on risks and opportunities.
• Create predictive models to improve the management of climate risks and their potential effects on the company.
• Perform scenario analysis and simulations to support sustainable decision-making.

KEY THEMES AND CONTENT

• Natural language processing tools for ESG monitoring, the extraction of relevant information, and risk and opportunity assessment.
• Predictive models for climate resilience in business, ML for climate risk management, and supply chain impacts.
• Scenario analysis: Monte Carlo simulations, scenario planning for sustainability decisions.

This module explores how sustainability can be a driver of innovation within companies, using digital and analytical tools to foster the transition to more sustainable business models.

OBJECTIVES

• Design sustainable transition plans: Implement transition strategies in key areas such as energy and waste management.
• Use AI to evaluate and improve sustainability plans.
• Promote sustainable business models: Explore and implement those models.

KEY THEMES AND CONTENT

• Transition plans (energy, supply chain, products and services, waste management), tools for informing strategic decision-making and AI tools for evaluating transition plans.
• Innovation in sustainable business models and their integration into the company's strategic decisions.

This module aims to teach how to integrate sustainability into financial decisions through the use of sustainable financial instruments, carbon markets and ESG portfolio analysis.

OBJECTIVES

• Become familiar with the available sustainable financial instruments:
• Understand green bonds, impact investing and sustainability-linked loans.
• Integrating carbon markets into finance: Learn how to incorporate emissions trading systems and Carbon Tax into financial strategy.
• Manage ESG portfolios: Apply ESG criteria in portfolio management to balance impact and profitability.

KEY THEMES AND CONTENT

• Sustainable financing instruments: Green bonds, impact investing and sustainability-linked loans.
• Integrating carbon markets into financial strategy: Emissions trading systems (ETS) and the cap and trade approach, Carbon Tax.
• ESG portfolio analysis based on sustainable-impact and financial-return objectives.
• Assessment of sustainability-related financial risks.

Explore how leading sustainability companies integrate responsible practices into their business models, through sessions with experts from pioneering companies in the sector.

OBJECTIVES

• Explore examples of how leading companies adopt sustainability as a strategic element for greater profitability and competitiveness.
• Identify good practices in different industries by applying sustainability criteria and new business models.

KEY THEMES AND CONTENT

• Sustainable business models: Case studies of outstanding companies in sustainability.
• Challenges and opportunities in sustainability: Problems encountered and solutions found by companies.
• Innovation and sustainability: How sustainability can drive growth and competitiveness.

Students will work on a capstone project, analyzing real-world ESG datasets for a selected company or industry. They will apply the knowledge and techniques learned throughout the program to optimize sustainability performance, presenting their results in a structured report.

OBJECTIVES

• Apply data analysis techniques to solve a real sustainability problem.
• Integrate the knowledge gained in the various modules to create a comprehensive sustainability strategy.
• Present actionable ideas and recommendations to stakeholders.

KEY THEMES AND CONTENT

• 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 viable, data-driven strategies to improve sustainability performance.
• Learning methods and assessments.
• Project-based learning: Practical application of knowledge through real-world case studies, group projects and sustainability simulations.
• Workshops and guest lectures: Industry experts provide insights into the latest trends, tools and techniques in sustainability data analysis.

Capstone project

Relevant sustainability capstones, where students solve real and urgent environmental problems using data-driven methods.