FORK 1014 Preparatory Course in Mathematics for Data Science
FORK 1014 Preparatory Course in Mathematics for Data Science
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.
By the end of the course, the student:

Will have knowledge of elementary probability theory and linear algebra in ndimensional Euclidean space.
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.
By the end of the course, the student:
Will be comfortable using basic tools and manipulating objects from probability and linear algebra.
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
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.

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

Teaching 
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