OUR ACADEMIC DEPARTEMENTS |
Lesson details
 | BIG DATA AND ACCOUNTING | ||
2018-2019 | EnIESEG School of Management
(
IÉSEG
)
| ||
Class code : | 1819-IÉSEG-M1S2-ACC-MA-EI73UE | ACCOUNTING / AUDIT / CONTROL | |
| Level | Year | Period | Language of instruction |
|---|---|---|---|
| Master | 1 | S2 | EnEnglish |
| Academic responsibility | C.BEUSELINCK |
|---|---|
| Lecturer(s) | C.BEUSELINCK |
- This class exists in these courses :
- IÉSEG > IESEG Degree - Programme Grande École > Semester 2 > 2,00 ECTS
Prerequisites
The participants should have an intermediate understanding of organisational aspects, internal and external audit and IT knowledge.
Learning outcomes
At the end of the course, the student should be able to:
* Have a sound knowledge of business intelligence and big data concepts
* Understand different tools for storage, retrieval and analysis for financial reporting and audit purposes
* Understand the importance of legacy technologies for big data handling
* Deal with structured and unstructured databases in a reporting environment
* Interpret data visualization metrics and output (Tableau ©)
* Formulate management-supportive recommendations based on financial reporting data analysis
Course description
The access to better and faster technology is reshaping the way but also the quality of audit and financial reporting work.
Big 4 audit firms as well individual corporations are ramping up investments at a significant pace to increase their market shares and at the same time to respond to increasinly tough stances by regulators on reporting failures.
This course provide you with an insightful view in the way business intelligence and big data focus can help businesses in reaping the benefits of the contemporaneous technology-driven reporting environment.
This course is not designed to turn accountants into programmers, but rather to prepare them to interpret big data and communicate well about this with top management decision-makers on the one hand and IT programmers on the other hand.
Class type
Class structure
| Type of course | Numbers of hours | Comments | |
|---|---|---|---|
| Independent work | |||
| E-Learning | 4,00 | ||
| Research | 4,00 | ||
| Independent study | |||
| Estimated personal workload | 8,00 | ||
| Group Project | 16,00 | ||
| Face to face | |||
| Interactive class | 16,00 | ||
| Total student workload | 48,00 | ||
Teaching methods
- Case study
- E-learning
- Interactive class
- Presentation
Assessment
In class participation and Group assignments.
| Type of control | Duration | Number | Percentage break-down |
|---|---|---|---|
| Continuous assessment | |||
| Oral presentation | 0,00 | 0 | 20,00 |
| Participation | 0,00 | 0 | 20,00 |
| Others | |||
| Case study | 0,00 | 0 | 60,00 |
| TOTAL | 100,00 | ||
Recommended reading
- All course material will be provided by IESEG Online -
- REVIEWS: Berg, A., Buffie, E. & L-F Zana (2018) "Should we fear the robot revolution? (The correct answer is yes)" Journal of Monetary Economics 97: pp. 117–148 -
* This information is non-binding and can be subject to change
