Corso di laurea: Data science for management Programmazione per l'A.A. 2019/2020

1.The objectives of the module aim at acquiring knowledge about: i) microfoundations of the economic behavior of individuals and firms and the understanding of markets; ii) economy-wide and large-scale phenomena and economic factors.
2.On completion, the student will be able: i) to interpret and address real-world microeconomic and macroeconomic problems; ii) to understand the open questions and issues on real-world economic problems.
3. On completion, students will able to understand the complexity and the trade-offs related to different market institutions and alternative economic policy measures.
4.On completion, students will be able how to present the results from the economic analyses, and which conclusions can be drawn from the analyses.
5.On completion, students will be able to address new questions and more sophisticated frameworks in economics.

1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards

1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference.
3.On completion, students will able to extract knowledge from data through statistical analyses in R.
4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R.
5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.

1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards

1.The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2.On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3.On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4.On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5.On completion, the student will be able to understand the structure of unsupervised learning.

1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.

2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field.

3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands.

4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts.

5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.

Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries.
Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management.
This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives.
The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem.
Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management.
Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills).
Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.