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2019/2020  KAN-CCMVV1740U  Data Science for Accounting and Auditing

English Title
Data Science for Accounting and Auditing

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 60
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Michael Werner - Department of Accounting (AA)
Main academic disciplines
  • Information technology
  • Accounting
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 14-02-2019

Relevant links

Learning objectives
  • Explain the meaning and role of data science in the context of accounting and auditing.
  • Explain fundamental aspects of data organization and analysis.
  • Demonstrate an understanding of selected data science methods to analyse accounting- and audit-relevant data.
  • Critically assess limitations of different analysis techniques and gained analysis results.
  • Critically reflect on ethical and legal aspects accompanied with the use of data analysis techniques.
Course prerequisites
- Students should have basic knowledge in financial and management accounting.
- Specific knowledge in auditing is not required as relevant fundamental aspects will be discussed during the course. A background in auditing is nevertheless helpful to achieve deeper understanding of the relevant topics.
- It is recommended but not mandatory that students have attended a course in accounting information systems, data management or any other comparable course.
Data Science for Accounting and Auditing:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Autumn
Aids Limited aids, see the list below:
The student is allowed to bring
  • Non-programmable, financial calculators: HP10bll+ or Texas BA II Plus
  • Language dictionaries in paper format
The student will have access to
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Course content, structure and pedagogical approach

Modern organizations suffer from phenomena such as data explosion and information overload. Data scientists have emerged as a new type of high-ranking professionals with the training and curiosity to make discoveries in the world of big data. Data science is an interdisciplinary field aiming to turn data into real value. Data may be company-internal or external, structured or unstructured, big or small, static or volatile. Data science deals with data extraction, preparation, exploration, transformation, storage, retrieval, computing, mining, learning, presenting, explaining and predicting.

This course focusses on the implications of data science in the context of accounting and auditing. A high demand exists in the marketplace for professionals that are able to deal with and to analyse large and heterogeneous data especially in the context of accounting and auditing. This practice-oriented course aims to provide interested students an entry into data science. It focusses on implications for accounting and auditing by primarily relying on examples and exercises that relate to financial accounting, management accounting and auditing.


The focus does not lie on the mathematical foundations of advanced statistical methods that we will use. Instead it focusses on the application of selected data analysis methods and the necessary theoretical background that is required to effectively apply these techniques and to interpret the gained information critically.


The course covers topics such as fundamental aspects of data organisation, retrieval and visualisation, characteristics of data science with an overview of descriptive, predictive and prescriptive analytics, as well as theoretical foundations and practical applications of selected data analysis techniques.


Description of the teaching methods
Lectures, demos, computer workshops
Feedback during the teaching period
During office hours and workshops
Student workload
Lectures 33 hours
Preparation for exam, classes and exercises 173 hours
Last updated on 14-02-2019