FORK 1014 Preparatory Course in Mathematics for Data Science

FORK 1014 Preparatory Course in Mathematics for Data Science

Course code: 
FORK 1014
Department: 
Data Science and Analytics
Credits: 
0
Course coordinator: 
Adam Lee
Course name in Norwegian: 
Preparatory Course in Mathematics for Data Science
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

This course provides a recap of the required background ideas and tools from probability and linear algebra which will be used during various courses in the MSc Data Science for Business.

Learning outcomes - Knowledge

By the end of the course, the student:

  • Will have knowledge of elementary probability theory and linear algebra in n-dimensional Euclidean space.

Learning outcomes - Skills

By the end of the course, the student:

  • Can perform common operations involving vectors and matrices (e.g. transpose, inverse, matrix multiplication, solving linear systems).
  • Can perform common operations involving random variables and vectors (e.g. calculating probabilities of events, calculating [conditional] expectations).
  • Can apply their knowledge of techniques from probability and linear algebra to solve problems in data science.
General Competence

By the end of the course, the student:

Will be comfortable using basic tools and manipulating objects from probability and linear algebra.

Course content

Linear Algebra:

  • Euclidean space
  • Span, linear independence and bases
  • Matrices and linear transformations
  • Positive (semi-) definite matrices
  • Vector subspaces, inner products and orthogonal projections
  • Eigenvalues and Eigenvectors

Probability:

  • Probability foundations
  • Random variables and their distributions
  • Expectations
  • Conditioning & independence
  • Limit theorems
Teaching and learning activities

The learning activities will be 100% lectures, with no asychronous hours. Students are expected to prepare for the lectures by reading assigned materials and participate actively in the discussion of the lecture topics.

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

-

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.

Required prerequisite knowledge

Calculus & ideally some prior exposure to probability and linear algebra.

Type of Assessment: 
None
Total weight: 
0
Student workload
ActivityDurationComment
Teaching
Sum workload: 
0

Text for 0 credits

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