The Master aims to achieve a highly qualified training program aimed at meeting the growing interest and the needs represented by the market, and in particular by companies, for the figure of “Data Scientist” (DS), a professional figure with a very strong interest which acts as a link between current Data Analysts and Statisticians, with deeper corporate, economic, legal and IT skills and knowledge.
The training course aims, in particular, to bridge the gap between supply and demand, proposing the paying institutions as active subjects for the training of the professional figure of the “Data Scientist”. The figure of the Data Scientist has recently been identified, but it proves increasingly essential for the development of competitive actions by companies in global markets. In fact, this professional figure is an ideal link between some of the most sought after figures in the recruitment market: analysts, managers, IT experts, experts used to working with data but, in this case, in large or small business contexts that are.
Data Scientist is a professional figure with high technical, technological and managerial skills who, at the same time, has solid statistical and data analysis skills, knows the information society in depth and has a strong corporate culture; not only quantitative analysis, therefore – as an analyst would do, especially on Big Data – but on their translation and presentation in an organized form, in order to make them legible, interpretable and usable for pre, and not ex post, as it is .
Strengthened by its technical preparation of analysis and statistics – fundamental, but not exclusive – this professionalism is able to relate with the two departments, both technical and administrative, effectively speaking both languages, and promoting effective coordination between different structures.
This last feature highlights how the Data Scientist must also be a communicator, with skills on basic techniques and on new languages and tools for communication.
Organic and Designed data: Date, Big Data and Open Data. Examples, context and technological scenarios;
From raw data to analysis: data refining and data modeling. Data Storage Technologies: Relational Databases, No-Sql Database;
Statistical Data Analysis: Data Mining, Pattern Discovery Methodologies, Predictive Analysis and Modeling;
World Wide Web and Linked Data ;
Management of unstructured data;
Data and law ;
Data in the company: from theory to vision;
The new corporate communication;