Insegnamento a.a. 2022-2023

30599 - COMPUTATIONAL APPLICATIONS IN ACCOUNTING

Dipartimento di Accounting

Insegnamento impartito in lingua italiana
CLEAM (3 cfu - I sem. - OP  |  SECS-P/07) - CLEF (3 cfu - I sem. - OP  |  SECS-P/07) - CLEACC (3 cfu - I sem. - OP  |  SECS-P/07) - BESS-CLES (3 cfu - I sem. - OP  |  SECS-P/07) - WBB (3 cfu - I sem. - OP  |  SECS-P/07) - BIEF (3 cfu - I sem. - OP  |  SECS-P/07) - BIEM (3 cfu - I sem. - OP  |  SECS-P/07) - BIG (3 cfu - I sem. - OP  |  SECS-P/07) - BEMACS (3 cfu - I sem. - OP  |  SECS-P/07) - BAI (3 cfu - I sem. - OP  |  SECS-P/07)
Docente responsabile dell'insegnamento:
FRANCESCO GROSSETTI

Classi: 31 (I/II sem.)
Docenti responsabili delle classi:
Classe 31: FRANCESCO GROSSETTI


Conoscenze pregresse consigliate

Some knowledge of Python and R is recommended.

Mission e Programma sintetico

MISSION

Our world's perception is mostly driven by data. The mission of this course is to teach students of quantitative disciplines how to tackle social sciences problems. In particular, the course will deal with accounting-based scenarios in which a combination of technical skills with institutional background knowledge is key.

PROGRAMMA SINTETICO

  • A very gentle introduction to Accounting.
  • Accounting Databases and their role.
  • Standard statistical approaches in Accounting.
  • Natural Language Processing in Accounting.
  • Processing visual information in Accounting.

Risultati di Apprendimento Attesi (RAA)

CONOSCENZA E COMPRENSIONE

Al termine dell'insegnamento, lo studente sarà in grado di...
  • Recognized the main statistical models generally adopted in the context of Accounting.
  • Understand the complexity of analyzing textual data.

CAPACITA' DI APPLICARE CONOSCENZA E COMPRENSIONE

Al termine dell'insegnamento, lo studente sarà in grado di...
  • Solve business problems by data-analytic thinking.
  • Use several tools and techniques to practically implement solution methods.
  • Use R to carry out simple statistical analyses and visualizations.
  • Prepare and discuss a scientific report.

Modalità didattiche

  • Lezioni frontali
  • Esercitazioni (esercizi, banche dati, software etc.)
  • Lavori/Assignment di gruppo

DETTAGLI

  • Reviews of programming lectures are given to students for home studying.
  • A practical group assignment is presented in class at the end of the course.

Metodi di valutazione dell'apprendimento

  Accertamento in itinere Prove parziali Prova generale
  • Assignment di gruppo (relazione, esercizio, dimostrazione, progetto etc.)
     x

STUDENTI FREQUENTANTI E NON FREQUENTANTI

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Materiali didattici


STUDENTI FREQUENTANTI E NON FREQUENTANTI

Main source:

  • Slides provided by the instructor.
  • Papers will also be circulated by the instructor.

 

Additional sources:

  • J. SILGE, D. ROBINSON (edition by O'REALLY),Text Mining with R: A Tidy Approach.
  • G. GROLEMUND, H. WICKHAM (edition by O'REALLY), R for Data Science.
  • G. GROLEMUND (edition by O'Really), Hands-On Programming with R: Write Your Own Functions and Simulations.

 

Advanced readings:

  • Trevor Hastie, Robert Tibshirani, Jerome Friedman:The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (available in pdf here: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf

  • F. CHOLLET (edition by Manning Publications), Deep Learning with R.
  • F. CHOLLET (edition by Manning Publications), Deep Learning with Python.
Modificato il 08/06/2022 09:52