Course 2024-2025 a.y.

30599 - COMPUTATIONAL APPLICATIONS IN ACCOUNTING

Department of Accounting

Course taught in English

Class timetable
Exam timetable
Go to class group/s: 31
CLEAM (3 credits - I sem. - OP  |  SECS-P/07) - CLEF (3 credits - I sem. - OP  |  SECS-P/07) - CLEACC (3 credits - I sem. - OP  |  SECS-P/07) - BESS-CLES (3 credits - I sem. - OP  |  SECS-P/07) - WBB (3 credits - I sem. - OP  |  SECS-P/07) - BIEF (3 credits - I sem. - OP  |  SECS-P/07) - BIEM (3 credits - I sem. - OP  |  SECS-P/07) - BIG (3 credits - I sem. - OP  |  SECS-P/07) - BEMACS (3 credits - I sem. - OP  |  SECS-P/07) - BAI (3 credits - I sem. - OP  |  SECS-P/07)
Course Director:
FRANCESCO GROSSETTI

Classes: 31 (I sem.)
Instructors:
Class 31: FRANCESCO GROSSETTI


Suggested background knowledge

Some knowledge of R is recommended.

Mission & Content Summary

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.

CONTENT SUMMARY

  • A very gentle introduction to Accounting.
  • Accounting Databases and their role.
  • Blockchain and Distributed Ledger Systems
  • Finite Mixture Models and Latent Class Models
  • Functional Data Analysis
  • Time to Event Data
  • Natural Language Processing with Examples

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Recognized the main statistical models generally adopted in the context of Accounting.
  • Increase the skill set with specific models. 
  • Understand the complexity of analyzing textual data.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • 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. 

Teaching methods

  • Lectures
  • Collaborative Works / Assignments

DETAILS

  • 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.

Assessment methods

  Continuous assessment Partial exams General exam
  • Collaborative Works / Assignment (report, exercise, presentation, project work etc.)
 x    

ATTENDING AND NOT ATTENDING STUDENTS

  • A group project delivered in the form of an in-class presentation at the end of the course will be evaluated.
  • The presentation must be about one of the papers circulated by the instructor. 

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

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.
Last change 27/05/2024 09:52