ELE 3781 Mathematics - Elective

ELE 3781 Mathematics - Elective

Course code: 
ELE 3781
Department: 
Economics
Credits: 
7.5
Course coordinator: 
Eivind Eriksen
Product category: 
Bachelor
Portfolio: 
Bachelor - Electives
Semester: 
2020 Autumn
Active status: 
Active
Teaching language: 
English
Course type: 
One semester
Introduction

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. In particular, it will introduce the students to modelling mathematical problems in Python, and teach hands-on skills. Topics include linear algebra and matrix methods, complex numbers, optimisation in several real variables, differential and difference equations, and optimal control theory.

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, and to use Python to model, solve and visualize mathematical problems. 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
  • Complex numbers
  • Optimization in several real variables
  • Differential and difference equations
  • Optimal control theory
Teaching and learning activities

The course is taught over one semester, and consists of lectures (45 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 and Python is used in lectures and problem solving sessions to illustrate taught material. Python will be used to implement mathematical models and visualize their solutions.

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

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 students own responsibility to obtain
any information provided in class.

All parts of the assessment must be passed in order to receive a final grade in the course.

Required prerequisite knowledge

EBA 2910 Mathematics for Business Analytics or equivalent.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
ELE 37811
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
20No1 Week(s)Individual Mid-term examination in form of an assignment.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
ELE 37812
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
80Yes3 Hour(s)
  • BI-approved exam calculator
  • Simple calculator
  • Monolingual dictionary, English-English
  • Bilingual dictionary, Native tongue - English
Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:20
Invigilation:No
Grouping (size):Individual
Support materials:
Duration:1 Week(s)
Comment:Mid-term examination in form of an assignment.
Exam code:ELE 37811
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:80
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • BI-approved exam calculator
  • Simple calculator
  • Monolingual dictionary, English-English
  • Bilingual dictionary, Native tongue - English
Duration:3 Hour(s)
Comment:
Exam code:ELE 37812
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
100
Course codeCredit reduction
GRA 6035100
Credit reductions:
Course code:GRA 6035
Credit reduction:100
Reduction description

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

Student workload
ActivityDurationComment
Teaching on Campus
45 Hour(s)
Seminar groups
12 Hour(s)
Plenary sessions for GRA 6035 Mathematics (in the afternoon)
Student's own work with learning resources
83 Hour(s)
Group work / Assignments
40 Hour(s)
Examination
20 Hour(s)
Mid-term assignment and final exam.
Sum workload: 
200

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.