EBA 3630 Data Driven Management Accounting

EBA 3630 Data Driven Management Accounting

Course code: 
EBA 3630
Accounting and Operations Management
Course coordinator: 
Elisabeth Plietzsch
Zhenyang Shi
Course name in Norwegian: 
Data Driven Management Accounting
Product category: 
Bachelor of Data Science for Business - Programme Courses
2023 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

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, variance analysis, performance measurement and to conduct forensic accounting.

Learning outcomes - Knowledge

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
Learning outcomes - Skills

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
General Competence
  • Evaluate the meaningfulness of certain types of data to the problem being analyzed
  • Understand the benefits and limitations of certain management accounting tools
Course content
  • In-house data
  • ERP systems
  • External relevant data
  • Extraction of data
  • Decision analysis
  • Management science
  • Cost estimation for various profitability calculations
  • Customer profitability analysis
  • Accounting forensics
  • Predictive analytics
  • Optimisation techniques 
  • Visualisation of management accounting analytics
Teaching and learning activities

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.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

The exam in this course will change from spring 2024 to:
70% individual 48 hours submission
30 % individual 24 hours submission

There will be arranged re-sit examination in EBA 36301 autumn 2023 and spring 2024.


Higher Education Entrance Qualification


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

Students are expected to have a fundamental understanding of Excel, Python and SQL programmes/languages, and had fundamental courses in management accounting and financial accounting.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
EBA 36301
Grading scale:
Grading rules:
Internal and external examiner
Examination every semester
100No72 Hour(s)Individual
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Individual
Duration:72 Hour(s)
Exam code: EBA 36301
Grading scale:ECTS
Resit:Examination every semester
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
Seminar groups
Prepare for teaching
Student's own work with learning resources
Including exam preparation
Sum workload: 

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.