stm4psd probability and statistics for data science

PROBABILITY AND STATISTICS FOR DATA SCIENCE

STM4PSD

2020

Credit points: 15

Subject outline

This subject develops an understanding of probability and statistics applied to Data Science. Probability topics include joint and conditional probability, Bayes' Theorem and distributions such as the uniform, binomial, Poisson and normal distributions as well as properties of random variables and the Central Limit Theorem. Statistical inference and data analysis is also considered covering, among other topics, significance testing and confidence intervals with an introduction to methods such as ANOVA, linear and nonlinear regression and model verification. Applications to data science are considered and students will be exposed to the R statistical package as well as the mathematical type-setting package LaTeX.

SchoolEngineering and Mathematical Sciences (Pre 2022)

Credit points15

Subject Co-ordinatorMitra Jazayeri

Available to Study Abroad/Exchange StudentsYes

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

Exchange StudentsNo

Available as ElectiveYes

Learning ActivitiesN/A

Capstone subjectNo

Subject particulars

Subject rules

PrerequisitesN/A

Co-requisitesN/A

Incompatible subjectsSTA4SS OR STM4PM

Equivalent subjectsN/A

Quota Management StrategyN/A

Quota-conditions or rulesN/A

Special conditionsThis subject will be offered to sufficient enrolment numbers

Minimum credit point requirementN/A

Assumed knowledgeN/A

Learning resources

Online learning materials

Resource TypeWeb resource

Resource RequirementPrescribed

AuthorLuke Prendergast

Year2017

Edition/VolumeN/A

PublisherLa Trobe University

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. Identify probabilistic traits of data science problems and choose methods which can be employed to determine valid and informative solutions.

02. Defend or question the validity of probability models applied to data science problems

03. Demonstrate an ability to solve a variety of Data Science problems using applications of probability models.

04. Define a statistical hypothesis with applications to Data Science that may be tested using data.

05. Identify and apply statistical methods for hypothesis testing and estimation with applications in Data Science.

06. Present clear, well-structured summaries of findings, both probabilistic and data-based, using appropriate mathematical and statistical vocabulary.

Subject options

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Melbourne (Bundoora), 2020, Semester 1, Blended

Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment informationN/A

Subject Instance Co-ordinatorMitra Jazayeri

Class requirements

Computer LaboratoryWeek: 10 - 22
One 2.00 hours computer laboratory 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.
Pre-recorded Lecture

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
Four written assignments (500-words equivalent each, 2,000-words total)Calculations and associated written discussion and conclusions.	N/A	N/A	N/A	No	40	SILO1, SILO2, SILO3, SILO4, SILO5, SILO6
3 hour final exam (3000-words equivalent)Following release of results, papers can be reviewed in accordance with University policy.	N/A	N/A	N/A	No	60	SILO1, SILO2, SILO3, SILO4, SILO5, SILO6

Melbourne (Bundoora), 2020, LTU Term 5, Blended