Bachelor of Mathematics (Data Science)

Entry Requirements

Applicants are required to meet one of the following criteria with a competitive result, and demonstrate that they fulfil any prerequisite requirements and essential requirements for admission:

Recent secondary education:

Meet any prerequisite requirements with a minimum grade of C- or equivalent and

Complete secondary qualifications equivalent to SACE, or

Complete the International Baccalaureate Diploma with a minimum score of 24 points

or

Higher education study

Complete or partly complete a recognized higher education program at a recognized higher education institution

or

Vocational Education and Training (VET)

Complete an award from a registered training organization at Certificate IV or above

or

Work and life experience

Hold completed secondary qualifications equivalent to SACE obtained more than 2 years in the past

English language requirements:

IELTS score of 6.0; TOEFL iBT score of 60 with Reading and Writing not less than 18; TOEFL paper-based test (PBT) score of 550 with TWE of 4.5; Cambridge CAE/CPE score of 169; Pearson's test of English (Academic) (PTE) score of 50 with Reading and Writing communicative scores not less than 50; CELUSA score of AE4.

Course Details

Data scientists are in increasing demand globally1. More and more organisations seek to analyse and interpret vast amounts of data and make sure it is used in intelligent, valuable ways.

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 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) (Industrial and Applied Mathematics), a Bachelor of Information Technology (Honours), a Master of Data Science or eventually a PhD.

What you'll learn

In first year you’ll study core subjects in maths and IT. You will focus on building your mathematical and programming skills with courses that include calculus, statistical methods, fundamentals of programming, and databases.

You will then move into your applied data science studies. You’ll study cross-disciplinary areas such as web development, data structures and mathematical communication, and mathematical modelling.

In third year you’ll combine study and hands-on experience with courses in programming and networking, project management, and analytics. You will also complete an ICT industry-based project to strengthen your abilities in research, analysis, and interpretation of data.

Your career

The field of data science field is evolving at a rapid rate. It will continue to grow as savvy business leaders integrate analytics into every facet of their organisations. Analytics, maths, science, data, and reasoning are becoming embedded into decision-making processes, every day and everywhere in the business world.

Careers to consider:

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 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
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