EBA 3630 Data Driven Management Accounting
EBA 3630 Data Driven Management Accounting
Management accountants have for many years been the drivers of analysis for decision-making in most organisations. Previously using reports from internal ERP systems, management accountants have extracted data, conducted spreadsheets, manual analysis, and arrived at a basis for providing relevant information to key decision-makers.
With the vast increase in available data, the management accountant is forced into a world previously hosted by computer scientists. In many cases, management accountants now have to extract the data themselves from various locations, filter and regroup data themselves, and then perform analysis to shed light on current decisions. This course intends to show how data from various sources within companies can be used to perform such tasks as cost estimation, customer profitability analysis,performance measurement, predictive analytics, and to conduct forensic accounting.
During the course, students should have learned/acquired knowledge of:
- The importance of external and internal data sources to solve management accounting problems
- Relevant management accounting tools
- How to translate management accounting problems into Python Code and/or other relevant programming languages
- How to use data for management decision making
After completing the course, students shall be able to:
- Identify relevant data
- Scope a management accounting problem
- Apply the correct management accounting tool
- Translate management accounting problems into Python Code and/or other relevant programming language
- Conduct financial accounting forensic analysis
- Communicate and interpret the results of using management accounting tools
- Derive recommendations for actions based on their analysis
- Evaluate the meaningfulness of certain types of data to the problem being analyzed
- Understand the benefits and limitations of certain management accounting tools
- In-house data
- External relevant data
- Extraction of data
- Decision analysis
- Management science
- Cost estimation for various profitability calculations
- Customer profitability analysis
- Accounting forensics
- Predictive analytics
- Visualisation of management accounting analytics
The course will consist of a mix of lectures and workshops where students do on-hand analysis. Students are expected to use Excel, Python and SQL to solve problems.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Students are expected to have a fundamental understanding of Excel, Python and SQL programmes/languages, and had a fundamental course in Accounting and Finance.
| Assessments |
|---|
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 70 Grouping: Individual Duration: 48 Hour(s) Exam code: EBA 36302 Grading scale: ECTS Resit: Examination every semester |
Exam category: Submission Form of assessment: Submission PDF Exam/hand-in semester: First Semester Weight: 30 Grouping: Individual Duration: 24 Hour(s) Exam code: EBA 36303 Grading scale: ECTS Resit: Examination every semester |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 20 | |
Seminar groups | 16 | |
Prepare for teaching | 100 | |
Student's own work with learning resources | 14 | |
Examination | 50 | Including exam preparation |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.