Composition Partnership

• Università degli Studi di Napoli Federico II, Italy (Coordinator and Host Institution)
• Athens University of Economics and Business, Greece (Sending Institution)
• Technische Universitat Dortmund, Germany (Sending Institution)
• University of Twente, Netherlands (Sending Institution)
• University College Dublin, Ireland (Sending Institution)
• University of Economics, Bratislava (Sending institution)
• Babes-Bolyai University Cluj-Napoca, Romania

Topic of the program

This program provides advanced training in statistical and machine learning techniques for sustainable finance. A key component is the Hybrid Approach for the Analysis of Complex Data Structures, where participants will learn to combine traditional statistical methods with modern computational tools to address sustainability-related financial challenges.

The course focuses on the integration of Environmental, Social, and Governance (ESG) factors into financial decision-making, risk management, and investment strategies. Participants will explore hybrid methods to analyze complex data from various sources, including ESG metrics, financial time series, and corporate reports, using tools like R and Python.

Learning Outcomes

By the end of the program, participants will:

1. Apply hybrid approaches to analyze complex data structures in sustainable finance.

2. Build statistical and machine learning models to assess ESG factors and their financial implications.

3. Understand and integrate sustainability metrics into financial decision-making.

4. Perform advanced data wrangling, cleaning, and analysis for financial datasets.

5. Utilize dashboards and reporting frameworks to present actionable insights.

Schedule description

a. Description of the physical component

Dates: 07.09.2026 to 11.09.2026
Location: Naples, University of Naples Federico II

Structure:

September 7, 2026

        Seminar - Principles of Sustainable Finance

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September 8, 2026

        Hybrid Models for Sustainable Finance.

        Topics: Overview of hybrid approaches, ESG data structures, and model integration.

        Evening. Laboratory lectures – supervised tutorial, individual and team work

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September 9, 2026

        Statistical Methods for ESG Analysis.

        Topics: Regression models, dimension reduction, and clustering techniques.

        Evening. Laboratory lectures – supervised tutorial, individual and team workù

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September 10, 2026

        Machine Learning and AI in Sustainable Finance.

        Topics: Predictive modeling, risk analysis, and green investment strategies.

        Evening. Laboratory lectures – supervised tutorial, individual and team workù

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September 11, 2026

        Corporate Sustainability Analytics.

        Topics: Hybrid methods for assessing corporate performance, reporting frameworks, and impact measurement.

        Evening. Laboratory lectures – supervised tutorial, individual and team workù
 

b. Description of the virtual component 

The programme includes online sessions delivered via Microsoft Teams. These virtual meetings will be used for lectures, discussions, supervision, and interaction with participants.

Link to the virtual sessions:

https://teams.microsoft.com/l/team/19:jIkS-HBZ-PnwCbAhwhRvcdnZIKMHKJ4irFOeKGXs3dQ1@thread.tacv2/conversations?groupId=6f6a6aee-0bd4-44c4-a847-9fd9c2bc160f&tenantId=2fcfe26a-bb62-46b0-b1e3-28f9da0c45fd

Structure:

August 26, 2026

• 10:00 CET– Welcome and introductions by the academic partners to greet the students

• 10:20–12:00 CET – Lecture: Hybrid Data Analysis for Sustainability

This lecture introduces the concept of hybrid data analysis in the context of sustainability-related decision-making. It explores how quantitative and qualitative data, structured and unstructured information, and different analytical approaches can be combined to address complex environmental, social, and economic challenges. Participants will gain insight into key sustainability data sources, common methodological approaches, and the role of expert knowledge in hybrid analytical frameworks.        

Marcos R. Machado, University of Twente, The Netherlands

September 2, 2026

• 10:00–12:00 CET – Lecture: Data Preprocessing Treatment & Applications in Sustainability

This course provides an introduction to the theoretical foundations and practical applications of data preprocessing in data mining and analytics. Participants will examine the main stages of preprocessing, including data cleaning, transformation, reduction, and other preparation techniques that enhance the quality and usability of data for subsequent analysis. The course also explores different categories of preprocessing methods and demonstrates their implementation through Python and Jupyter Notebooks. A sustainability-related application case is included to illustrate how preprocessing techniques can be applied in practice to support meaningful and robust data-driven insights.

Marcos R. Machado, University of Twente, The Netherlands

Wouter van Heeswijk, University of Twente, The Netherland

1. Session 2: ESG Data Wrangling and Integration.
2. Session 3: Building Dashboards for Sustainable Finance.
3. Session 4: Final Presentations and Keynote on Hybrid Methods in Finance.

Key Features

• Level: Master’s and PhD students.
• ECTS: 3
• Language: English
• Online Support: Weekly mentorship from sustainability and finance experts.
• Access to materials: https://www.labstat.it/courses

Hybrid Approach for Complex Data Structures

This course focuses on blending traditional statistical approaches (e.g., regression, dimension reduction) with machine learning and AI methods (e.g., clustering, predictive analytics) to address the multifaceted challenges in sustainable finance. Emphasis is placed on handling large-scale, heterogeneous datasets, developing scalable models, and deriving actionable insights for ESG evaluation and decision-making.

Practical information

- Level of students: Master and PhD students

- Number of ECTS: 3 

- Main language of instruction/training: English

- Venue of Activities (City, Institution): Naples, Department of Political Sciences, University of Naples Federico II (Statistics Laboratory and G4 room)

In addition, a tutor will be available for each participant during the training period and a dedicated programme manager provided by BIP Faculty will be available virtually. These figures will act as support in the learning and skills development phase.