ELE 3781 Mathematics - Elective

ELE 3781 Mathematics - Elective

Course code: 
ELE 3781
Course coordinator: 
Eivind Eriksen
Course name in Norwegian: 
Mathematics - Elective
Product category: 
Bachelor - Electives
2023 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This elective course is based on the Master's course GRA 6035 Mathematics, and is intended for students at the programme Bachelor of Data Science for Business, aiming for admission to the Master of Data Science for Business.

The language of mathematics is extensively used to analyse problems in business, economics and finance, and mathematical models, theories, and methods are extensively used to solve problems. The mathematical requirements of students in Business Analytics go beyond the material usually taught in other undergraduate courses, and this elective course will teach the student more advanced mathematical models, theories, and methods. Topics include linear algebra and matrix methods, optimisation in several real variables, differential and difference equations.

Learning outcomes - Knowledge

After completing the course, the student will have advanced knowledge of mathematical concepts, models, theories, and methods. The student will have an advanced understanding of linear algebra and matrix methods, complex numbers, optimisation in several real variables, differential and difference equations and optimal control theory, and specialized understanding of how these mathematical models and methods can be used in business, economics and finance.

Learning outcomes - Skills

After completing the course, the student will be able to analyse quantitative problems using the mathematical language, and be able to use mathematical models and methods to solve these problems. The student will be able to assess solution strategies, be able to carry out necessary computations correctly and precisely. The student will be able to give mathematical arguments for his conclusions, and be able to formulate written answers that explain the methods used and interpret the solutions obtained.

General Competence

After completing the course, the student will be able to reflect upon central assumptions for the models and theories used, and critically assess if they are met in applications. The student will be capable of critical thinking. The student will be able to reflect upon the results obtained, and critically assess if they are reasonable.

Course content
  • Linear algebra and matrix methods
  • Optimization in several real variables
  • Differential and difference equations
Teaching and learning activities

The course is taught over one semester, and consists of lectures (36 hours) and plenary problem solving sessions (12 hours).

For each lecture, there will be a work program consisting of exercises and reading assignments. The student must learn the material presented in the reading assignments, and work through the exercises. Some of the exercises will be reviewed in lectures and plenary problem solving sessions. It is assumed that the student has worked with the exercises in order to take full advantage of the review. By allocating some time in class to short assignment related to new topics, students will be activated and learning objectives achieved.

Wolfram Alpha is used in lectures and problem solving sessions to illustrate taught material. 

Software tools
No specified computer-based tools are required.
Additional information

Starting in the autumn of 2023, the form of evaluation in this course has changed from two exam codes (ELE 37811 and ELE 37812) to one exam code (ELE 37813).

A last re-sit examination is offered in the former exam codes ELE 37811 and ELE 37812 in autumn 2023.

For electives re-sit is normally offered at the next scheduled course. If an elective is discontinued or is not initiated in the semester it is offered, re-sit will be offered in the electives ordinary semester.

Please note that while attendance is not compulsory in all courses, it is the student's own responsibility to obtain any information provided in class.


Higher Education Entrance Qualification


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

Required prerequisite knowledge

EBA 2910 Mathematics for Business Analytics, EBA 1180 Mathenatics for Data Science or equivalent.

Students from the following programs, which aim for admission to the Master of Data Science for Business, can take the course if they have prior knowledge corresponding to the basic course in mathematics plus ELE 3719 Matematikk valgfag or ELE 3776 Mathematical Analysis, or the basic course MET1180 Matematikk for siviløkonomer. This applies to:

  • Bachelor i Finans
  • Bachelor i økonomi og administrasjon
  • Bachelor i økonomi og ledelse (siv.øk)
  • Bachelor of Business Administration

For these programmes, students are also required to take courses from Bachelor of Data Science for Business programme, in order to apply for admission to the Master of Data Science for Business programme.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
ELE 37813
Grading scale:
Grading rules:
Internal examiner
Examination when next scheduled course
100Yes5 Hour(s)
  • No support materials
Individual .
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Individual
Support materials:
  • No support materials
Duration:5 Hour(s)
Exam code:ELE 37813
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Reduction description

This course overlaps 100% with GRA 6035 Mathematics, which is a Core course in the Master's programs.

Student workload
36 Hour(s)
Seminar groups
12 Hour(s)
Plenary sessions for GRA 6035 Mathematics (in the afternoon)
Student's own work with learning resources
80 Hour(s)
Group work / Assignments
67 Hour(s)
5 Hour(s)
Final exam.
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.