PROBABILITY AND STATISTICS FOR DATA SCIENCE

STM4PSD

2019

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.

School: School Engineering&Mathematical Sciences

Credit points: 15

Subject Co-ordinator: Mitra Jazayeri

Available to Study Abroad Students: Yes

Subject year level: Year Level 4 - UG/Hons/1st Yr PG

Exchange Students: Yes

Subject particulars

Subject rules

Prerequisites: Must be admitted into the Master of Data Science (SMDS)

Co-requisites: N/A

Incompatible subjects: STA4SS, STM4PM

Equivalent subjects: N/A

Special conditions: This subject will be offered to sufficient enrolment numbers

Learning resources

Readings

Resource Type	Title	Resource Requirement	Author and Year	Publisher
Readings	Online learning materials	Prescribed	Luke Prendergast 2017	La Trobe University

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.

Activities:: Modeled in online readings and videos and practised in tutorials.

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

Activities:: Modeled in online readings and videos and practised in tutorials.

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

Activities:: Modeled in online readings and videos and practised in tutorials.

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

Activities:: Modeled in online readings and videos and practised in tutorials.

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

Activities:: Modeled in online readings and videos and practised in tutorials.

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

Activities:: Modeled in online readings and videos and practised in tutorials.

Melbourne, 2019, Semester 1, Blended

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information:

Subject Instance Co-ordinator: Mitra Jazayeri

Class requirements

Unscheduled Online ClassWeek: 10 - 22
One 2.0 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"

Computer LaboratoryWeek: 10 - 22
One 2.0 hours computer laboratory per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment element	Comments	%	ILO*
Four written assignments (500-words equivalent each, 2,000-words total)	Calculations and associated written discussion and conclusions.	40	01, 02, 03, 04, 05, 06
2.5 hour final exam (2,500-words equivalent)	Following release of results, papers can be reviewed in accordance with University policy.	60	01, 02, 03, 04, 05, 06

Melbourne, 2019, Semester 2, Blended