FORK 1005 Preparatory course in Mathematics for Statistics and Data Analytics

FORK 1005 Preparatory course in Mathematics for Statistics and Data Analytics

Course code: 
FORK 1005
Department: 
Economics
Credits: 
0
Course coordinator: 
Steffen Grønneberg
Course name in Norwegian: 
Preparatory course in Mathematics for Statistics and Data Analytics
Product category: 
Master
Portfolio: 
Master - Preparatory course
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

We review central mathematical concepts that are especially relevant for statistics at the master level.

At the start of autumn semester, the course will be given for students in the following programmes:

  • Msc in Leadership and Organizational Psychology
  • Msc in Strategic Marketing Management

At the start of spring semester, the course will be given for students taking a MSc in Business with the following specializations:

  • Business Law, Tax and Accounting
  • Leadership and Change
  • Logistics - Supply Chain and Network
  • Marketing
  • Strategy

This course is not relevant for students doing MSc in Finance or MSc in Business, major Finance or Economics.

Learning outcomes - Knowledge

To provide the basic mathematical tools for multivariate statistics courses to students who need to refresh their mathematical skills.

Learning outcomes - Skills

-

General Competence

-

Course content

The course reviews the mathematical prerequisites required to understand applied statistics at the level of multiple linear regression.

Teaching and learning activities

There will be 10 teaching hours in the course.

Software tools
No specified computer-based tools are required.
Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Disclaimer

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

Type of Assessment: 
None
Total weight: 
0
Student workload
ActivityDurationComment
Teaching
10 Hour(s)
Lectures
Sum workload: 
10

Text for 0 credits