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.

SchoolEngineering and Mathematical Sciences

Credit points15

Subject Co-ordinatorKatherine Seaton

Available to Study Abroad/Exchange StudentsNo

Subject year levelYear Level 4 - UG/Hons/1st Yr PG

Available as ElectiveNo

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

Prerequisites Must be admitted in SMDS or SMIOTB or HMSA

Co-requisitesN/A

Incompatible subjectsMAT4NLA OR MAT4CDE

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsN/A

Minimum credit point requirementN/A

Assumed knowledgeN/A

Career Ready

Career-focusedNo

Work-based learningNo

Self sourced or Uni sourcedN/A

Entire subject or partial subjectN/A

Total hours/days requiredN/A

Location of WBL activity (region)N/A

WBL addtional requirementsN/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.

Subject options

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Start date between: and    Key dates

Bendigo, 2020, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorKatherine Seaton

Class requirements

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

Unscheduled Online Class Week: 10 - 22
One 2.00 h 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 elementCommentsCategoryContributionHurdle% ILO*
Four written assignments (750-words each, 3000-words total) These assignments are problem-based and show consolidation of mathematical skills.N/AN/AN/ANo35 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/AN/AN/ANo15 SILO3, SILO4
Two hour exam (2,000-words equivalent)N/AN/AN/ANo50 SILO1, SILO2, SILO4

Melbourne (Bundoora), 2020, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorKatherine Seaton

Class requirements

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

Unscheduled Online Class Week: 10 - 22
One 2.00 h unscheduled online class per week on any day including weekend during the day from week 10 to week 22 and delivered via online.
"Readings and videos in LMS"

Assessments

Assessment elementCommentsCategoryContributionHurdle% ILO*
Four written assignments (750-words each, 3000-words total) These assignments are problem-based and show consolidation of mathematical skills.N/AN/AN/ANo35 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/AN/AN/ANo15 SILO3, SILO4
Two hour exam (2,000-words equivalent)N/AN/AN/ANo50 SILO1, SILO2, SILO4