# PROBABILITY AND STATISTICS FOR DATA SCIENCE

STM4PSD

2018

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.

SchoolSchool Engineering&Mathematical Sciences

Credit points15

Subject Co-ordinatorLuke Prendergast

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

Exchange StudentsYes

## Subject particulars

### Subject rules

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

Co-requisitesN/A

Incompatible subjects STA4SS, STM4PM

Equivalent subjectsN/A

Special conditionsN/A

Resource TypeTitleResource RequirementAuthor and YearPublisher
ReadingsOnline learning materialsPrescribedLuke Prendergast 2017La 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.

## Subject options

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Start date between: and    Key dates

## Melbourne, 2018, Semester 1, Blended

### Overview

Online enrolmentYes

Maximum enrolment sizeN/A

Enrolment information

Subject Instance Co-ordinatorLuke Prendergast

### Class requirements

Unscheduled Online Class Week: 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 Laboratory Week: 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.