Objectives and competences
In the course, the student systematically acquires knowledge in the field of data analytics, which is related to performing activities in the field of accounting and auditing, especially for business decision-making needs. It is knowledge about the use and usefulness of data analytics in the field of accounting in various organizations.
Content (Syllabus outline)
• Data analytics for accounting and identification of problems
• Pre-processing data, performing tests and analysing the results
• Communication and visualisation of results
• The modern accounting environment
• Financial statements analytics
• Managerial analytics
• Audit data analytics
Learning and teaching methods
Lectures and discussions in the classroom. Studying examples and solving tasks, including discussion. Independent study of the student.
Intended learning outcomes - knowledge and understanding
In this course, students:
1. Gain theoretical and practical knowledge about the approaches, methods, and techniques of data analytics in accounting, its purpose and users, as well as the professional environment and regulation. (PILO 2a)
2. They are trained to use theoretical knowledge in practical cases of data analytics in accounting. (PILO 2a)
3. They are trained in the pre-processing of data from the field of accounting and their further processing and analysis. (PILO 1a)
4. They solve problems in the field of business economics in connection with data analytics in accounting; they connect different concepts. (PILO 3a, PILO 3b)
5. They are trained to recognize a problem from a business and ethical point of view in order to formulate proposals for responsible business decision-making. (PILO 4a)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Intended learning outcomes - transferable/key skills and other attributes
In this course, students:
1. Gain theoretical and practical knowledge about the approaches, methods, and techniques of data analytics in accounting, its purpose and users, as well as the professional environment and regulation. (PILO 2a)
2. They are trained to use theoretical knowledge in practical cases of data analytics in accounting. (PILO 2a)
3. They are trained in the pre-processing of data from the field of accounting and their further processing and analysis. (PILO 1a)
4. They solve problems in the field of business economics in connection with data analytics in accounting; they connect different concepts. (PILO 3a, PILO 3b)
5. They are trained to recognize a problem from a business and ethical point of view in order to formulate proposals for responsible business decision-making. (PILO 4a)
The PILO label (i.e., Programme Intended Learning Outcomes) defines the contribution of each listed intended learning outcome of a course towards achieving the general and/or subject-specific competencies or learning outcomes acquired through the programme.
Readings
• Guide to Audit Data Analytics. (2018). AICPA.
Dodatni viri/Additional Readings
• Vernon Richardson, Ryan Teeter, Katie Terrell. (2023). Data Analytics for Accounting, 3rd Edition. McGraw Hill.
• Ann C. Dzuranin, Guido Geerts, Margarita Lenk. (2022). Data and Analytics in Accounting: An Integrated Approach, 1st Edition. Wiley.
• Tarek Rana, Jan Svanberg, Peter Öhman, Alan Lowe (2023). Handbook of Big Data and Analytics in Accounting and Auditing. Springer.
• Jim Lindell. (2020). Analytics and Big Data for Accountants, 2nd Edition. AICPA.
Additional information on implementation and assessment Final exam (100%)
Final exam - individual written exam with many questions
