# MATHEMATICS FOR DATA SCIENCE

MAT4MDS

2021

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-ordinatorHien Nguyen

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 Students must be admitted in the following course: SMDS

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-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

## Graduate capabilities & intended learning outcomes

### 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

Select to view your study options…

Start date between: and    Key dates

## Bendigo, 2021, Semester 1, Blended

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorToen 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 elementCategoryContributionHurdle% ILO*

Four written assignments (750-words each, 3000-words total). These assignments are problem-based and show consolidation of mathematical skills.

AssignmentIndividualNo35 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.

OtherIndividualNo15 SILO3, SILO4

Two hour exam (2,000-words equivalent)

Central examIndividualNo50 SILO1, SILO2, SILO4

## Melbourne (Bundoora), 2021, Semester 1, Blended

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorHien Nguyen

### 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.
Readings and videos in LMS

### Assessments

Assessment elementCategoryContributionHurdle% ILO*

Four written assignments (750-words each, 3000-words total). These assignments are problem-based and show consolidation of mathematical skills.

AssignmentIndividualNo35 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.

OtherIndividualNo15 SILO3, SILO4

Two hour exam (2,000-words equivalent)

Central examIndividualNo50 SILO1, SILO2, SILO4

## Melbourne (Bundoora), 2021, Semester 2, Blended

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Subject Instance Co-ordinatorHien Nguyen

### Class requirements

PracticalWeek: 30 - 42
One 2.00 hours practical per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.

Unscheduled Online ClassWeek: 30 - 42
One 2.00 hours unscheduled online class per week on any day including weekend during the day from week 30 to week 42 and delivered via online.
Readings and videos in LMS

### Assessments

Assessment elementCategoryContributionHurdle% ILO*

Four written assignments (750-words each, 3000-words total). These assignments are problem-based and show consolidation of mathematical skills.

AssignmentIndividualNo35 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.

OtherIndividualNo15 SILO3, SILO4

Two hour exam (2,000-words equivalent)

Central examIndividualNo50 SILO1, SILO2, SILO4