FORK 1005 Preparatory course in Mathematics for Statistics and Data Analytics
FORK 1005 Preparatory course in Mathematics for Statistics and Data Analytics
We review central mathematical concepts that are especially relevant for multivariate statistics.
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.
To provide the basic mathematical tools for multivariate statistics courses to students who need to refresh their mathematical skills.
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The course reviews the mathematical theory surrounding applied statistics at the level of multiple linear regression. This includes calculation rules with the summation notation, standardization of random variables and its use for Normally distributed random variables, and related mathematical issues.
There will be 10 teaching hours in the course.
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 spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 10 Hour(s) | Lectures |
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