Data Science Major

Code MAAAJJ

Focuses on the numeracy and analytical repertoire of graduates.

Graduates will gain statistical expertise in analysing and visualising simple and complex data sets from a variety of sources such as disease markers, genomic and transcriptomic data and business data sets. Graduates will be competitive in the data science job market as it is implemented in computing, business and the natural sciences.

Special entry requirements

All applicants are required to have a final School letter grade of C or higher in Mathematics Methods ATAR (Year 11), or successful completion of MAT1108 Foundations of Mathematics, with equivalents considered.

Code

MAAAJJ

Availability & Campus

JO	ML	BU	OL

JO - Joondalup BU - South West ML - Mount Lawley OL - Online

Details

Structure

Unit Code	Unit Title	Credit Points
MAT1137	Introductory Applied Mathematics	15
MAT1252	Mathematics for Computing	15
CSP1150	Programming Principles	15
CSG1207	Systems and Database Design	15
MAT2110	Applied Statistics	15
MAT2440	Time Series Forecasting	15
MAT3110	Applied Multivariate Statistics	15
MAT3120	Machine Learning and Data Visualisation	15

For more detailed unit information for this Major take a look at our Handbook. To organise your life for next semester visit the Semester timetable.

Student handbook

Notes

Learning Outcomes

Apply broad data science knowledge to a range of theoretical and practical big data situations.
Collecting and communicating data science analyses sensitively and ethically with respect for cultural diversity, including indigenous cultural competence.
Demonstrate autonomy, accountability and judgment to manage own learning and professional practice.
Exhibit communication skills in statistics discourse across a range of topics/contexts of data science.
Think creatively or anticipate challenges to generate solutions to big data problems.
Think critically to analyse, interpret and conceptualise big data science problems.
Use digital technologies, information literacies and numeracy proficiency to access, evaluate and synthesise relevant information from multiple sources.
Work collaboratively and demonstrate initiative to implement social, sustainable and ethical values.