Big Data Analytics for Accounting and Finance

Python programming language is used in the course. Previous coding experience is useful, but not a prerequisition. However, the course assignments include programming components.

Recommended preliminary course for data analytics and Python programming:

• LASK2057 Introduction to analytics in accounting

Technology requirements: laptop

By the end of the course students will achieve the following intended learning outcomes:

• Students will demonstrate solid knowledge of different topics in the area of data science taking into consideration the special features of accounting/finance datasets
• Students will solve complex practical research problems in accounting and finance using modern data science research environments
• Students will apply advance machine learning methods to analyse large unstructured and strucured datasets important in accounting and finance
• Students will demonstrate research skills by conducting a research project with a comprehensive set of data analytics and machine learning methods, problem-solving skills and detailed analysis of the results
  -Students can utilize generative AI efficiently in data science

• Online study resources defined by the instructor
• Datasets distributed by the instructor
• Lecture notes
  Parts of the following books:
  Wes McKinney (2017), Python for Data Analysis, 2nd ed., O’Reilly Media
  Francois Chollet (2017), Deep Learning with Python, 1st ed., Manning Publications
  Stephen Marsland (2014), Machine Learning - An Algorithmic Perspective, 2nd ed., Chapman and Hall/CRC

This course is primarily for accounting students, but there is also quota for finance students. The total number of participants is restricted (75).