Corso di laurea: Data science for management Programmazione per l'A.A. 2020/2021

The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.

The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.

1. Knowledge and understanding (Conoscenza e capacità di comprensione). 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. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). 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. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.

DATA BASE
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
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. The learning objectives are as follows:
Knowledge and understanding
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

This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.

The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria.
The goals of the course are:

Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems.
Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems.
Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions.
Communication skills: students will acquire base communication and reading skills using technical language.
Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.

Knowledge and understanding (Conoscenza e capacità di comprensione). The students will learn the basic concepts behind the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). The 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.
Making judgements (Autonomia di giudizio). The students will learn to indepedently analyse data to extract knowledge from it through statistical analyses in R.
Communication skills (Abilità comunicative). The students will acquire the necessary communication skills and the appropriate use of technical language to present the results from the statistical analyses, based on the use of the statistical software R.
Learning skills (Capacità di apprendimento). The students will learn to use the statistical software R for basic data analysis and modeling. They will also acquire the competences needed to learn new data analysis and presentation techniques through the statistical software R.

The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.

he course covers the fundamental concepts of management and design of database systems.

Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.

The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.

1. Knowledge and understanding (Conoscenza e capacità di comprensione). 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. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). 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. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.

DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). 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. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). 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. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.

STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.

Knowledge and understanding

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. Applying knowledge and understanding

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. Making judgements

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. Communication skills

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. Learning skills

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.