Bachelor of Mathematics (Data Science)

Course Overview

Duration
3 YEARS
Scholarship
YES
Fee
AUD$ 37,100 PER ANNUM
Intake
FEBRUARY JULY

Information and data are being generated faster than ever before, with the trend driven by advances in computing power, exponential growth in internet use and increased cloud computing. Organisations can benefit significantly from the analysis of this data, resulting in growing demand for data science experts to inform and drive business decisions1.

This degree is designed to produce job-ready graduates to meet this industry need, and to fill the growing range of work opportunities in the market. Successful maths and data scientists draw on skills from a range of complementary disciplines, so this degree offers a balanced mix of mathematics, information technology and data science. In your final year you’ll have the chance to complete an industry-based project to experience real-world challenges and gain workplace experience.

You will graduate ready to work in a data science role in industry or the public sector. Because data science is also a tool that supports research across an increasing range of disciplines, you could also choose to continue with a Bachelor of Applied Science (Honours) (Mathematics), a Bachelor of Information Technology (Honours), or eventually a PhD.

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Entry Requirements

ENGLISH LANGUAGE REQUIREMENTS

IELTS total [6.0]

IELTS reading [6.0]

IELTS writing [6.0]

Career Outcomes

our career

The growth and diversity of data available is allowing companies to integrate analytics into their daily operations, triggering the development of analytics roles in a broad range of sectors, and subsequently increasing demand for people with data science skills1. Graduates can consider the following careers:

data scientist: understanding interfaces, data migrations, big data and databases; taking the lead in processing raw data and determining the best types of analysis; mining large volumes of data to understand user behaviours and interactions; communicating data findings to IT leadership and business leaders to promote innovation

big data researcher: extracting data from relational databases; manipulating and exploring data using quantitative, statistical and visualisation tools; selecting appropriate modelling techniques so predictive models are developed using rigorous statistical processes; maintaining effective processes for validating and updating predictive models

big data visualiser: using visualisation software to analyse data, drawing implications and communicating findings; providing input on database requirements for reporting/analytics; acquiring, managing and documenting data (e.g. geo-spatial); creating visualisations from data or GIS data analysis

data miner: collecting data from numerous databases; helping businesses to make decisions about how data should be analysed in areas such as expenses, profitability, and for other important business decisions

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