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Course Topics
Module 1 is designed to provide specific professional skills for advanced programming in Python. The students will learn how to develop a Python program, starting from designing it, and going through coding, testing and validation. They will master Python in its full object-oriented features, learning how to develop complex programs that are well structured, and make use of techniques for code re-use, pipelining, maintenance, and deployment. Module 2 is designed to acquire professional skills and knowledge useful when dealing with large-scale datasets. In particular, the students will master data collection, exploration, transformation, curation, analysis, and visualization, choosing the most appropriate technique for the data at hand. They will make insights from the data, supported by a rigorous data science pipeline, which starts with raw data, produces machine learning models, and ends with advanced visualizations. This module, addresses common pitfalls that can mislead the analysis and makes extensive use of specialized Python libraries, acquiring the best practices of reproducible, data-driven analysis and research.

Teaching format
The course adopts a blended, student-centered approach that emphasizes problem-based learning and active engagement. Selected lecture content is made available online in advance, enabling students to explore key concepts independently and at their own pace. This preparatory work allows in-person sessions to focus on applying knowledge through problem-solving, collaborative activities, and guided discussions—fostering critical thinking and deeper understanding. The teaching format combines frontal lectures, hands-on lab assignments, and project work, ensuring that students develop both theoretical knowledge and practical skills in python programming, and in data analysis, modelling, and visualization. The course is aligned with the principles of the EDUNEXT initiative (https://edunext.eu), promoted by Italian universities, which supports the integration of digital resources and active learning strategies in higher education.

Educational objectives
Intended Learning Outcomes (ILO) M1: ILO 1 Knowledge and understanding: ILO 1.1 The student acquires programming knowledge, particularly aimed at data analysis and statistical methodologies for implementing models as well as analysing large-scale datasets. In particular, the computing skills are focused on machine learning methods, on understanding modern techniques for data management and storage, including data from heterogeneous sources in terms of type and structure, such as spatio-temporal data and high-dimensional data, also in cloud environments, and on implementing algorithms for massive data processing. ILO 2 Applying knowledge and understanding: ILO 2.1 Students will develop the ability to apply and implement techniques for analysing large-scale datasets and spatio-temporal data under conditions of uncertainty, through the design and development of algorithms. The goal is to ensure the utility, quality, and effectiveness of the analysis. ILO 2.2 Ability to use IT technologies, techniques and methodologies for the acquisition, management, integration, analysis and visualisation of large datasets, in order to ensure scalability in terms of dataset volume and acquisition speed. These skills relate in particular to large database and dataset management systems and related visualisation techniques, models and languages for expressing data semantics, learning techniques, decision-making models, information systems organisation, web search techniques and data flow management techniques. ILO 3 Making judgements: ILO 3.1 The student acquires the ability to apply acquired knowledge to interpret data in order to make directional and operational decisions in a business context. ILO4 Communication skills: ILO 4.1 The student acquires the ability to communicate effectively in oral and written form the specialised content of the individual disciplines, using different registers, depending on the recipients and the communicative and didactic purposes, and to evaluate the formative effects of his/her communication. ILO 5 Learning skills: ILO 5.1 The student acquires knowledge of scientific research tools. He/she will also be able to make autonomous use of information technology to carry out bibliographic research and investigations both for his/her own training and for further education. Furthermore, through the curricular teaching and the activities related to the preparation of the final thesis, she will be able to acquire the ability - to identify thematic connections and to establish relationships between methods of analysis and application contexts; - to frame a new problem in a systematic manner and to implement appropriate analysis solutions; - to formulate general statistical-econometric models from the phenomena studied M2: ILO 1 Knowledge and understanding: ILO 1.1 The student acquires programming knowledge, particularly aimed at data analysis and statistical methodologies for implementing models as well as analysing large-scale datasets. In particular, the computing skills are focused on machine learning methods, on understanding modern techniques for data management and storage, including data from heterogeneous sources in terms of type and structure, such as spatio-temporal data and high-dimensional data, also in cloud environments, and on implementing algorithms for massive data processing. ILO 1.2 Students will acquire knowledge and skills in the analysis of textual data and network structures, with particular attention to issues related to data security and privacy. ILO 2 Applying knowledge and understanding: ILO 2.1 Students will develop the ability to apply and implement techniques for analysing large-scale datasets and spatio-temporal data under conditions of uncertainty, through the design and development of algorithms. The goal is to ensure the utility, quality, and effectiveness of the analysis. ILO 2.2 Ability to use IT technologies, techniques and methodologies for the acquisition, management, integration, analysis and visualisation of large datasets, in order to ensure scalability in terms of dataset volume and acquisition speed. These skills relate in particular to large database and dataset management systems and related visualisation techniques, models and languages for expressing data semantics, learning techniques, decision-making models, information systems organisation, web search techniques and data flow management techniques. ILO 3 Making judgements: ILO 3.1 The student acquires the ability to apply acquired knowledge to interpret data in order to make directional and operational decisions in a business context. ILO 3.2 The student acquires the ability to apply acquired knowledge to support processes related to production, management and risk promotion activities and investment choices through the organisation, analysis and interpretation of complex databases. ILO4 Communication skills: ILO 4.1 The student acquires the ability to communicate effectively in oral and written form the specialised content of the individual disciplines, using different registers, depending on the recipients and the communicative and didactic purposes, and to evaluate the formative effects of his/her communication. ILO 5 Learning skills: ILO 5.1 The student acquires knowledge of scientific research tools. He/she will also be able to make autonomous use of information technology to carry out bibliographic research and investigations both for his/her own training and for further education. Furthermore, through the curricular teaching and the activities related to the preparation of the final thesis, she will be able to acquire the ability - to identify thematic connections and to establish relationships between methods of analysis and application contexts; - to frame a new problem in a systematic manner and to implement appropriate analysis solutions; - to formulate general statistical-econometric models from the phenomena studied.

Assessment
The exam modalities are the same for both the attending and the non-attending students. All project works must have been submitted, at the very latest, 15 days ahead of the oral exam. In case of a positive mark, the projects will count for all 3 regular exam sessions. Module 1: Project work (70% of the final grade) (ILOs 1, 2, 5). Oral exam (30% of the final grade) (ILOs 3, 4). Module 2: Project work (70% of the final grade) (ILOs 1, 2, 5). Oral exam (30% of the final grade) (ILOs 3, 4).

Evaluation criteria
70% project work, 30% oral exam. • Relevant for project work: clarity of presentation, ability to gain useful and novel insights from data, creativity, critical thinking, ability to adhere to reproducible research best practices • Ability to use Python to write, evaluate and deploy advanced, object-oriented computer programs • Ability to use Python to employ (understand, recall and use) data analytics methods in practical settings, from data collection and curation, to data analysis, modelling and visualization.