MATHEMATICS FOR DATA SCIENCE

MAT4MDS

2020

Credit points: 15

Subject outline

Important mathematical ideas which underpin the theory and techniques of data science are introduced and consolidated in this subject. Matrices are used to store and work with quantitative information, and the methods of calculus are used to find extreme values and accumulation. The Gamma and Beta functions are introduced, as are eigenvalues, eigenvectors and the rank of a matrix. Emphasis is placed on the relevance of the mathematics to data science applications (such as least squares estimators and calculation of variance in data), and on the development of clear communication in explaining technical ideas. This is a foundational subject for the Master of Data Science.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Hien Nguyen

Available to Study Abroad/Exchange Students: No

Subject year level: Year Level 4 - UG/Hons/1st Yr PG

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: Must be admitted in SMDS or SMIOTB or HMSA

Co-requisites: N/A

Incompatible subjects: MAT4CDE OR MAT4NLA

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: N/A

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Perform mathematical calculations relevant to data science fluently and accurately.

02. Creatively apply mathematical techniques to unfamiliar problems.

03. Apply mathematical skills to analysis of data science literature.

04. Present mathematical thinking in written form in a meaningful and succinct way using both words and mathematical notation.

Bendigo, 2020, Semester 1, Blended

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Toen Castle

Class requirements

PracticalWeek: 10 - 22
One 2.00 hours practical per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Unscheduled Online ClassWeek: 10 - 22
One 2.00 hours unscheduled online class per week on any day including weekend during the day from week 10 to week 22 and delivered via online.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
Four written assignments (750-words each, 3000-words total)These assignments are problem-based and show consolidation of mathematical skills.	N/A	N/A	N/A	No	35	SILO1, SILO2, SILO3, SILO4
Extended written task (1,500-words)An analysis of an issue or paper in data science from the mathematical perspective, presented in an extended written form.	N/A	N/A	N/A	No	15	SILO3, SILO4
Two hour exam (2,000-words equivalent)	N/A	N/A	N/A	No	50	SILO1, SILO2, SILO4

Melbourne (Bundoora), 2020, Semester 1, Blended