30599 - COMPUTATIONAL APPLICATIONS IN ACCOUNTING
Course taught in Italian
Class-group lessons delivered on campus
Some knowledge of Python and R is recommended.
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.
- 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.
- Recognized the main statistical models generally adopted in the context of Accounting.
- Understand the complexity of analyzing textual data.
- 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.
- Lezioni frontali
- Esercitazioni (esercizi, banche dati, software etc.)
- Lavori/Assignment di gruppo
- 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.
|Accertamento in itinere||Prove parziali||Prova generale|
- Slides provided by the instructor.
- Papers will also be circulated by the instructor.
- 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.
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.