Learning style

A successful data scientist is able to lead a work team taking the responsibility of key technical choices; she/he is expected to appraise results, as well as visualize and communicate them in an effective manner. The learning process will contribute to the development of this kind of skills by exposing each student to team-working, contributing to project work, checking their learning with professors, being involved into stage and writing a final dissertation. This dissertation will stem out of a work done either in the academy or at private companies or public bodies (internship).

Even though the student will specialise in the domain defined by the choosen curriculum, a data science career is interdisciplinary by nature, so every student will also learn how to interact with practitioners and scientists from different fields and contexts. She/he will be able to effectively capture the problem statement and present results. As well, very good communication and a strong attitude toward collaboration, along with the capability of exerting leadership, will be developed in a multi-disciplinary setting by means of lectures for peers attended by professors, reading seminars, and simulation of interviews with experts.

The main target of the learning process is to develop the ability to cope with new problems: this means that students will be trained in a "problem-oriented" mind-setting. She/he will be keen to update and enhance knowledge by keeping the pace of scientific and technological innovations, moving from a sound grounding in basic as well domain expertise. The development of the final dissertation will test all the acquired skills and will make each new data scientist able to operatively apply them on the job.