STATISTICAL COMPUTING
STM2SC
2021
Credit points: 15
Subject outline
Statistical Computing provides an introduction to methods of computational statistics. It is strongly focused on modern statistical approaches and computational methods and extensively uses R and Python software. All methods are introduced and demonstrated using real world data examples. The topics covered in this subject include optimization, Bayesian methods, MCMC, bootstrap, density estimation and clustering. In this subject, you will develop practical skills for data analysis in science, industry and business applications.
SchoolEngineering and Mathematical Sciences
Credit points15
Subject Co-ordinatorAndriy Olenko
Available to Study Abroad/Exchange StudentsYes
Subject year levelYear Level 2 - UG
Available as ElectiveNo
Learning ActivitiesN/A
Capstone subjectNo
Subject particulars
Subject rules
PrerequisitesBIO2POS OR STA1LS
Co-requisitesN/A
Incompatible subjectsN/A
Equivalent subjectsN/A
Quota Management StrategyN/A
Quota-conditions or rulesN/A
Special conditionsN/A
Minimum credit point requirementN/A
Assumed knowledgeN/A
Readings
Computational Statistics
Resource TypeRecommended
Resource RequirementN/A
AuthorGeof H. Givens, Jennifer A Hoeting
Year2012
Edition/VolumeN/A
PublisherWiley
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/A
Computational Statistics
Resource TypeRecommended
Resource RequirementN/A
AuthorJames E. Gentle
Year2009
Edition/VolumeN/A
PublisherSpringer
ISBNN/A
Chapter/article titleN/A
Chapter/issueN/A
URLN/A
Other descriptionN/A
Source locationN/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
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Melbourne (Bundoora), 2021, Semester 2, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Subject Instance Co-ordinatorAndriy Olenko
Class requirements
Computer LaboratoryWeek: 30 - 42
One 2.00 h computer laboratory per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.
LectureWeek: 30 - 42
One 2.00 h lecture per week on weekdays during the day from week 30 to week 42 and delivered via face-to-face.
Assessments
| Assessment element | Category | Contribution | Hurdle | % | ILO* |
|---|---|---|---|---|---|
5 Assignments (approx. 200 words each ) | N/A | N/A | No | 30 | SILO1, SILO2, SILO3, SILO4 |
3-hour short answer Final Examination (approx. 3000 words) | N/A | N/A | No | 70 | SILO1, SILO2, SILO3, SILO4 |