Informazioni di Digital Data Analysis | DidatticaWEB

Generali:

Dipartimento: Scienze Matematiche, Fisiche E Naturali
Settore Ministeriale: FIS/05
Codice di verbalizzazione: 8067178
Codice corso: 8067178
Metodi di insegnamento: Frontale E Altro
Metodi di valutazione: Orale
Prerequisiti: English The achievement of the learning outcomes needs a pre-existing knowledge about some topics of general physics, statistics, mathematical methods for physics, basic programming, and of some subjects of electronics.
Obiettivi: English LEARNING OUTCOMES: The aim of the course is to provide to the student a broad overview of the various methods and techniques of data analysis, with a deeper insight on those used in modern-time astrophysics. In particular, we will study the aspects of digital information access, handling, restoration, manipulation, compression and transformation into data. KNOWLEDGE AND UNDERSTANDING: Students must achieve a deep understanding of the implication of data digitization procedures and the associated effects. They must also achieve a good comprehension of the state of the art in data analysis techniques, in particular in the analysis of data from Physics and Astrophysics experiments. APPLYING KNOWLEDGE AND UNDERSTANDING: Students must be able to identify the key elements of a physical data-sets, with specific knowledge on the aspects of digital information access, handling, restoration, manipulation, compression and transformation into data, with focus on astrophysical data-sets. They must be able to use and implement algorithms for Data Restoring and Analysis, and for Machine Learning. They must be able to comprehend, critically evaluate, and adapt to new experimental data, existing algorithms. They must develop skills in critically evaluating the applicability limits of the different methodologies to the type of signal analyzed. Students must developed advanced skills in programming in Python or an analogous programming language. MAKING JUDGEMENTS: Students must be able to autonomously run data analysis algorithms and numerical simulations. They must be able to perform bibliographic researches on the topic of Data Analysis and Machine Learning and to autonomously select the relevant results, in particular on the Web. This capabilities are achieved through the study to prepare for the exam, during the laboratory activities and examining in depth specific topics by critically analyzing journal papers. COMMUNICATION SKILLS: Students must be able to work in an interdisciplinary group. They must be able to present their own research or the results of a bibliographic research both to the experts and to amateurs. LEARNING SKILLS: Students must be able to take on a new research field in Data Analysis or Machine Learning by autonomous study. In particular, we will study the aspects of digital information access, handling, restoration, manipulation, compression and transformation into data. All these aspects will be also tackled in the laboratory sessions, where the students will have the opportunity to work on actual astrophysics data-sets to implement the algorithm introduced in the lessons.

Didattica:

A.A.: 2026/2027
Canale: UNICO
Crediti: 8

Classe virtuale:

Nome classe: GIOVANNELLI-8067178-DIGITAL_DATA_ANALYSIS_4
Link Microsoft Teams: Link
Docente: GIOVANNELLI LUCA