mat4mds mathematics for data science
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-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 Must be admitted in SMDS or SMIOTB or HMSA
Co-requisitesN/A
Incompatible subjectsMAT4CDE OR MAT4NLA
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
Subject options
Select to view your study options…
Bendigo, 2020, 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 element | 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 | 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 | No | 15 | SILO3, SILO4 |
Two hour exam (2,000-words equivalent) | N/A | N/A | No | 50 | SILO1, SILO2, SILO4 |
Melbourne (Bundoora), 2020, 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 element | 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 | 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 | No | 15 | SILO3, SILO4 |
Two hour exam (2,000-words equivalent) | N/A | N/A | No | 50 | SILO1, SILO2, SILO4 |