Educational objectives
M1
ILO 1 Knowledge and understanding:
ILO 1.1 The student acquires knowledge of the analytical techniques and tools required to understand and quantitatively analyse economic and business phenomena in order to support decision-making processes.
ILO 1.2 The student consolidates knowledge of statistical inference, linear models and their generalisations, linear algebra, and optimisation techniques.
ILO 1.3 The student acquires an in-depth knowledge of the main techniques of supervised and unsupervised statistical learning, which are instrumental in the development of analysis and visualisation of economic and business data.
ILO 2 Applying knowledge and understanding:
ILO 2.1 Ability to apply and implement analysis techniques focusing on different types of datasets such as streaming data, tabular data, documents and images and analysis on joint datasets.
ILO 2.2 Ability to apply supervised and unsupervised learning, and knowledge modelling, extraction, integration, analysis and exploitation; these skills are declined in various application domains of interest to companies and public and private organisations.
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.
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.
Additional educational objectives and learning outcomes
M1
INTENDED LEARNING OUTCOMES (ILO)
ILO 1 – Knowledge and understanding
ILO 1.1
The student acquires knowledge and understanding of high-dimensional data and big data settings, including the curse of dimensionality and its implications for statistical modelling and inference.
ILO 1.2
The student acquires knowledge and understanding of convex criteria for model selection, model aggregation and model combination methods in high-dimensional contexts.
ILO 1.3
The student acquires knowledge and understanding of dimension reduction techniques, high-dimensional regression models, graphical models and multiple testing procedures.
ILO 1.4
The student acquires knowledge and understanding of the foundations of Natural Language Processing (NLP), including text representation, algorithmic text classification, sentiment analysis and information retrieval.
ILO 1.5
The student acquires knowledge and understanding of neural network approaches to NLP and language modelling, including the principles underlying large language models (LLMs) and their evaluation.
ILO 1.6
The student acquires knowledge and understanding of advanced NLP applications, including web-based data acquisition, prompt engineering, fine-tuning of pre-trained language models and emerging LLM-based systems.
ILO 2 – Applying knowledge and understanding
ILO 2.1
The student is able to analyse high-dimensional datasets, identify modelling challenges related to dimensionality, sparsity and multiple testing, and select appropriate statistical tools.
ILO 2.2
The student is able to apply convex model selection criteria, aggregation methods, dimension reduction techniques, high-dimensional regression and graphical models to complex data.
ILO 2.3
The student is able to implement NLP pipelines for text classification, sentiment analysis and information retrieval, and to evaluate their performance.
ILO 2.4
The student is able to use neural network–based NLP models, including pre-trained language models, and apply prompt engineering and fine-tuning techniques for specific tasks.
ILO 2.5
The student is able to acquire and preprocess textual data from web sources and integrate it into data analysis and NLP workflows.
ILO 3 – Making judgements
ILO 3.1
The student is able to critically evaluate modelling choices and results in both high-dimensional statistical analysis and NLP applications, taking into account assumptions, uncertainty and performance metrics.
ILO 3.2
The student is able to compare alternative statistical and NLP methods and select appropriate approaches based on data characteristics, objectives and computational constraints.
ILO 3.3
The student is able to use quantitative and algorithmic evidence to support analytical and operational decisions in data-driven applications.
ILO 4 – Communication skills
ILO 4.1
The student is able to communicate clearly and effectively, in oral and written form, the methods, results and limitations of high-dimensional statistical analyses and NLP systems, adapting the level of technical detail to different audiences.
ILO 5 – Learning skills
ILO 5.1
The student is able to autonomously deepen knowledge of high-dimensional statistical methods and NLP techniques, integrate new tools and methodologies, and systematically approach new and complex data analysis problems.