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

01. Apply appropriate computational statistical methods for data analysis.
02. Analyse properties of statistical models using computational methods.
03. Execute statistical software functionality for data analysis and appropriately interpret the output.
04. Explain the underlying principles of computational statistical methods using appropriate vocabulary and notation.

Subject options

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Start date between: and    Key dates

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 elementCommentsCategoryContributionHurdle% ILO*

5 Assignments (approx. 200 words each )

N/AN/AN/ANo30 SILO1, SILO2, SILO3, SILO4

3-hour short answer Final Examination (approx. 3000 words)

N/AN/AN/ANo70 SILO1, SILO2, SILO3, SILO4