MTH2019 Multivariate mathematics for data science
Faculty of Science
MTH2019 Multivariate mathematics for data science is a level 2, 6-credit-point, undergraduate unit from the Faculty of Science, offered in 2027 in Semester 1 at Clayton and Malaysia. It has no prerequisites and unlocks 6 units, leading on to 47 units in all.
- Credit points
- 6
- Offered in 2027
- Semester 1
- Clayton, Malaysia
- Assessment
- Exam 50%
- and 1 other task
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Requisites
Before MTH2019
No prerequisites or corequisites besides the enrolment rules below.
After MTH2019
6 units list MTH2019 as a prerequisite or corequisite.
- MTH2032Differential equations with modellingNo reviews yetCoreq
- MTH2051Introduction to computational mathematicsNo reviews yet
- MTH2222Mathematics of uncertaintyNo reviews yetCoreq
- MTH2232Mathematical statisticsNo reviews yetCoreq
- MTH3241Random processes in the sciences and engineeringNo reviews yet
- MTH3320Computational linear algebraNo reviews yet
Enrolment rules
COREQUISITE: Enrolment in Bachelor of Applied Data Science/Bachelor of Applied Data Science Advanced (Honours).
Overview
This unit introduces and develops a range of basic concepts and techniques related to two main subjects: multivariate calculus and linear algebra. The programme is targeted for students following a degree in data science and the material will have emphasis on assimilating important principles, on developing classical techniques, and on facilitating the use of the theoretical framework and practical methods in the context of common applicative problems. The unit will cover partial derivatives, extrema of multivariate functions, integration, linear transformations, matrices and orthogonalisation, eigenvalues and eigenvectors, and applications to data science.
Offerings in 2027
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Clayton | On campus |
| First semester | Malaysia | On campus |
Assessment
- Continuous assessmentDemonstration50%
- Final assessment - Exam (3 hours and 10 minutes)Examination50%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
Successfully describe, and apply multivariate mathematics to problems in data science;
- 2
Exhibit key skills in the calculus of functions of several variables including the computation of partial derivatives, finding tangent planes and identifying stationary points, root finding, and convexity for optimisation;
- 3
Compute line, surface and volume integrals in a range of coordinate systems;
- 4
Demonstrate their comprehension of basic concepts related to linear transformations and vector spaces, including subspace, span, linear independence, basis, kernel and range;
- 5
Diagonalise real matrices by computing their eigenvalues and finding their eigenspaces;
- 6
Explain and apply basic concepts related to vector spaces and subspaces;
- 7
Present clear mathematical arguments in written and oral form.
Workload and teaching
- Seminars36 hours
- Applied sessions22 hours
- Three 1-hour seminars;
- One 2-hour applied class (in weeks 2-12) and
- 7 hours of independent study per week.
Where it fits
MTH2019 is part of 1 area of study in the 2027 handbook.
Contacts
- Chief Examiners
- Dr Simon Clarke
- Unit Coordinators
- Dr Zeeshan Ali
- Dr Simon Clarke
Common questions
What are the prerequisites for MTH2019?
MTH2019 has no prerequisites, but enrolment rules apply.
What can I take after MTH2019?
MTH2019 is a prerequisite or corequisite for 6 units, including MTH2032, MTH2051, MTH2222, MTH2232, MTH3241 and MTH3320. Those lead on to 47 units in all.
When is MTH2019 offered?
In 2027, MTH2019 runs in Semester 1 at Clayton and Malaysia.
Does MTH2019 have an exam?
Yes. The exam is worth 50% of the final mark, alongside 1 other task.
Which majors and minors include MTH2019?
MTH2019 is part of Business analytics.