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Curriculum Data Science

From September 2023 the curriculum Data Science of the Master's Degree in Computer Science is transformed into the Master's Degree in Data Science, Scientific Computing & Artificial Intelligence ( in the LM DATA (Data Science) degree class

Teaching activities


The Big Data paradigm is becoming a key factor of innovation and competition at a global level.
Data Science is an interdisciplinary field of study, whose objects are the scientific methods, procedures and systems to extract knowledge, understanding and potential forecasts from large collections of data, either structured or not, while respecting the privacy of individuals. To this purpose, Data Science relies on theories and methods from diverse fields of Computer Science, Statistics and Mathematics, such as Algorithms, Classification, Data Mining, Database, Machine Learning, Numerical Methods, Optimization, Security.
Data Science lays down the scientific foundations of to:
  • identify and sample data sources;
  • organize and manage large datasets efficiently, taking into account constraints imposed by software, hardware and communication band;
  • build mathematical models to analyze hidden regularities and patterns in the data, or even learn from them;
  • ensure that data collection, transmission and analysis are conducted without risk to privacy;
  • create visualizations that help understanding data;
  • present and communicate knowledge derived from the data.
The Data Science Curriculum aims to provide a sound grounding on the techniques, and the underlying theoretical principles, which make data analysis possible. To this end, the curriculum combines and applies competences from different disciplinary areas active at our University, mainly from the areas of Computer Science, Information Engineering and Statistics. In particular, courses are offered focusing on the following aspects: 
  • algorithmic techniques for data analysis, with specific attention to structures for large data sets and related theoretical and practical aspects;
  • data mining algorithms for searching regularities and patterns in data, and data structures necessary for their organization;
  • cryptographic methods for protecting the privacy of individuals, during data collection, transmission and analysis;
  • basic and advanced algorithms for statistical learning, basics of computational learning theory, the design of solutions to real problems;
  • parallel and high performance programming techniques;
  • statistical bases of regression, Bayesian classification and inference, which are at the core of automatic learning;
  • numerical methods to acquire those computer aided geometric design skills useful for the implementation and use of specific algorithms for data visualization;
  • optimization methods, necessary to effectively conduct data analysis in the presence of constraints on hardware and software resources.
In the Information Society, Data Scientist is naturally emerging as one of the most sought after professions. According to a frequently cited study (McKinsey Global Institute, 2011), the demand for data scientists in 2018 could exceed their actual availability by 1.5 million.
Graduates in Data Science will have the necessary skills to apply to be employed by: companies that, on locally or globally operate in the field of market data analysis and "business intelligence"; institutions that process large collections of (medical, financial, census,...) data; small and large companies that rely on complex information systems to manage their activities.
 Here are some examples of emerging professions that fall within those areas: 
  • Data Management Professional: deals with the collection and management of data, and infrastructures that support these activities, similar to what the administrators do for traditional databases;
  • Data Engineer: deals with the design and development of the infrastructure;
  • Business Analyst: a role closely related to analysis - both in the traditional and Big Data sense - and data presentation, including the generation of reports and views, and everything that is generally referred to as "business intelligence";
  • Machine Learning Analyst: responsible for creating and applying prediction and correlation tools that extract knowledge, rules and forecasts from data;
  • Data-oriented Professional: at a higher level, identifies useful data for a given problem, chooses the appropriate analysis tools and selects from the extracted information what may be useful for solving the problem.
Graduates in Computer Science can enrol in the Italian Register of Information Engineers (Professional Register - Section A of Engineers - Information Sector) and apply for the PhD programmes in Computer Science.




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