MJRU-DATSC v.1 Data Science Major (BSc Science)
Major/Stream Overview
This major/stream is part of a larger course. Information is specific to the major/stream, please refer to the course for more information.
Every industry is using the increasing availability of large volumes of data to grow - from predicting weather patterns and optimising harvesting in agriculture, to improving patient diagnosis and treatment in the health industry, to enhancing the management of remote infrastructure in mining. Central to harnessing the power of data to drive innovation is the Data Scientist. The Data Science major is a multidisciplinary major with fields of study in computing, statistics, emerging internet technologies and media studies. Foundational studies in programming and statistics from the basis of higher level studies in data mining, data security and computer simulation. The major builds students’ capacity to extract, analyse and visualise large volumes of data and communicate analytical outcomes to a range of audiences. Graduates from the major will be equipped to enter a range of industries where data science is key to data-driven innovation.
Career Opportunities
A Major in Data Science leads to employment opportunities in the private and public sectors. You may pursue a career as a: Marketing and Advertising Data Analyst Pricing Analyst Financial Analyst Game Designer Health and Allied Health Data Analyst Business Intelligence Data Analyst Machine Learning Specialist Information Security Technologist Growth Analyst Information Technology Statistician
Additional Course Expenses
Students may be expected to purchase a number of textbooks and other essential study materials.
Major/Stream Entry and Completion Details
ATAR >75% and ATAR Mathematical Methods
Major/Minor/Stream Organisation
Major/Stream Learning Outcomes
A graduate of this course can:
1. understand the theoretical background to processes for efficient collection, management, secure storage and analysis of large data sets
2. formulate hypotheses about data and develop innovative strategies for testing them by implement appropriate algorithms to analyse both large and small datasets
3. extract valid and meaningful conclusions from various types of large data sets that can support evidence based decision making
4. communicate approaches and solutions to data science problems to a range of audiences in a variety of modes
5. identify, select and use appropriate open source and proprietary data management and analysis tools to identify patterns or relationships in large volumes of data
6. recognise the importance of continuous learning opportunities in a rapidly developing field and engage in self-driven development as a data scientist
7. understand the global nature of data science and apply appropriate international standards in data science and data analytics
8. work collaboratively and respectfully with data scientists from a range of cultural backgrounds
9. work professionally and ethically on independent data science projects and as a team member working collaboratively to innovative data science solutions
Duration and Availability
This Major is three years' full-time study or equivalent part-time study.
Course Structure | Hrs/Wk | Credit | |||
---|---|---|---|---|---|
Year 1 Semester 1 | |||||
STAT1003 | v.1 | Introduction to Data Science | 5.0 | 25.0 | |
COMP1005 | v.1 | Fundamentals of Programming | 4.0 | 25.0 | |
STAT1001 | v.1 | Statistical Probability | 3.0 | 12.5 | |
STAT1002 | v.1 | Statistical Data Analysis | 3.0 | 12.5 | |
SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
100.0 | |||||
Year 1 Semester 2 | |||||
MATH1015 | v.1 | Linear Algebra 1 | 4.0 | 25.0 | |
ISYS1001 | v.1 | Database Systems | 4.0 | 25.0 | |
COMS1000 | v.1 | Science Communications | 2.0 | 12.5 | |
STAT1000 | v.1 | Regression and non-Parametric Inference | 3.0 | 12.5 | |
SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
100.0 | |||||
Year 2 Semester 1 | |||||
ISEC2001 | v.2 | Fundamental Concepts of Data Security | 3.0 | 25.0 | |
STAT2005 | v.1 | Computer Simulation | 4.0 | 25.0 | |
MEDA3000 | v.2 | Mobile, Locative and Ubiquitous Media | 3.0 | 25.0 | |
SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
100.0 | |||||
Year 2 Semester 2 | |||||
COMP1002 | v.1 | Data Structures and Algorithms | 4.0 | 25.0 | |
ICTE2000 | v.2 | Interactive, Virtual and Immersive Environments | 3.0 | 25.0 | |
STAT2003 | v.1 | Analytics for Experimental and Simulated Data | 5.0 | 25.0 | |
SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
100.0 | |||||
Year 3 Semester 1 | |||||
COMP3006 | v.1 | Artificial and Machine Intelligence | 3.0 | 25.0 | |
COMP3001 | v.1 | Design and Analysis of Algorithms | 4.0 | 25.0 | |
CNCO3003 | v.1 | Mobile Cloud Computing | 3.0 | 25.0 | |
SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
100.0 | |||||
Year 3 Semester 2 | |||||
COMP3009 | v.1 | Data Mining | 3.0 | 25.0 | |
STAT2004 | v.1 | Analytics for Observational Data | 5.0 | 25.0 | |
SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
SELECT OPTIONAL UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
100.0 | |||||
Optional Units to Select from in Year 3 Semester 2 | Hrs/Wk | Credit | |||
MATH3004 | v.1 | Industrial Project | 4.0 | 25.0 | |
COMP3005 | v.1 | Computer Project 2 | 9.0 | 25.0 | |
MEDA3001 | v.2 | Major Digital Humanities Project | 3.0 | 25.0 | |
ISYS3002 | v.2 | Information Systems and Technology Project 2 | 2.0 | 25.0 |
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