Bildungsziele
Intended Learning Outcomes (ILO)
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 Students acquire programming skills specifically geared towards data analysis and statistical methodologies aimed at implementing models and analysing large datasets.
ILO 1.2 Students acquire knowledge of textual data and network analysis and aspects related to the security and privacy of such data.
ILO 2 Applying knowledge and understanding:
ILO 2.1 Ability to apply and implement, through the development of algorithms, techniques for analysing large datasets and spatial and temporal data, under conditions of uncertainty, in order to ensure the usefulness, 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 the volume and speed of dataset acquisition. 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 system 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.
Bildungsziele und erwartete Lernergebnisse (zus. Informationen)
M1
The course will provide students with the ability to analyze and interpret data using econometric models.
1) Knowledge and understanding.
The course will equip students with the ability to organize and combine economic and business data starting from structured databases. It will also enable students to acquire knowledge about state-of-the-art of models to represent time series data.
2) Applying knowledge and understanding:
Students will be able to implement data management techniques and econometric models in order to extract proper information from data, useful to analyse real phenomena in several fields of economics and management, and to understand their most important aspects.
3) Making judgements:
students who successfully complete this course will be able to select the most appropriate data management approaches and apply proficiently statistical model to obtain inferences and predictions using statistical software, and organize results in order to draw conclusions and decide in uncertain situations, like in specific economic and business situations.
4) Communication skills:
students who successfully complete this course will be able to communicate, to experts and non-experts the results of their analyses using specific software.
5) Learning skills:
the course is aimed to provide the methodological and applied knowledge of data management for subsequent econometric modeling, and necessary to address subsequent analyses.
M2
TBD