MJRH-ADDSC v.2 Data Science Major (BAdvSci) (Honours)
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.
This honours level major provides a flexible and personalised approach to studying data science with students able to explore the field through opportunities for immersive research experiences, industry placement and team-based projects. 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. This Data Science major is multidisciplinary 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
Students graduating with an honours degree in science can pursue careers in research through postgraduate degrees (Master by Research, PhD) but are also high competitive in industry. Graduates from the Bachelor of Advanced Science (Honours) have the added advantage being able to evidence the development of high level discipline, leadership and entrepreneurship skills through engagement with industry and research groups throughout their course. Graduates from the major will be equipped to enter a range of industries where data science is key to innovation. They have highly transferable skills that enable career flexibility and international employment opportunities. Data Scientists are employed in established and start-up tech companies, global business consultancies, the banking and finances sector, and many areas of government including CSIRO and ASIO.
Additional Course Expenses
Students may be expected to purchase a number of textbooks and other essential study materials. Students may require a laboratory coat.
Major/Stream Entry and Completion Details
Prerequisites: Mathematics Methods ATAR. Desirable: Mathematics Specialist ATAR.
Specific Course Completion Details
To progress to the final year of study and hence to qualify for this award students will be required to provide evidence of research experience totalling at least 96 hours.
Major/Minor/Stream Organisation
This major consists of core data science units, a set of core units focused on the development of research and leadership skills and attributes, and a final year capstone experience. In addition there is an elective stream that enables the opportunity to study further units in data analytics and visualisation, computing or a minor in Arts, Commerce or Science.
Major/Stream Learning Outcomes
A graduate of this course can:
1. demonstrate an advanced knowledge of the nature of science, its methods and processes, and an advanced knowledge of the theoretical background to processes for efficient collection, management, secure storage and analysis of large data sets
2. critically analyse challenging and multi-faceted problems in data science, formulating hypotheses about data and developing innovative strategies for testing them; 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, and incorporate them into the planning, conduct and communication of their own work
4. communicate approaches, ideas, findings and solutions to data science problems in a variety of modes to informed professional audiences
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 and address complex research questions
6. demonstrate intellectual independence and engage in self-driven continuous discipline and professional education and training as a data scientist
7. participate in the generation and application of science in addressing global problems while understanding the global nature of data science; apply appropriate international standards in data science and data analytics
8. work collaboratively and respectfully with data scientists from a range of cultural backgrounds and understand the importance of the cultural diversity and individual human rights that impact data science
9. be able to work as an independent data scientist and collaboratively within teams either as a professional leader or collaborator using effective problem solving and decision making skills within a professional context
Duration and Availability
This course is four years full-time or equivalent part-time study. One intake is offered each year in February.
| Course Structure | Hrs/Wk | Credit | |||
|---|---|---|---|---|---|
| Year 1 Semester 1 | |||||
| COMP1005 | v.1 | Fundamentals of Programming | 4.0 | 25.0 | |
| STAT1003 | v.1 | Introduction to Data Science | 5.0 | 25.0 | |
| NPSC1002 | v.1 | Science, Technology and Global Problems | 4.0 | 25.0 | |
| STAT1005 | v.1 | Introduction to Probability and Data Analysis | 3.0 | 25.0 | |
| 100.0 | |||||
| Year 1 Semester 2 | |||||
| STAT1006 | v.1 | Regression and Nonparametric Inference | 3.0 | 25.0 | |
| MATH1015 | v.1 | Linear Algebra 1 | 4.0 | 25.0 | |
| OR | |||||
| MATH1016 | v.1 | Calculus 1 | 5.0 | 25.0 | |
| ISYS1001 | v.1 | Database Systems | 4.0 | 25.0 | |
| SELECT OPTIONAL UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
| 100.0 | |||||
| Year 2 Full Year | |||||
| NPSC2001 | v.1 | Research, Leadership and Entrepreneurship in Science 1 | 7.0 | 50.0 | |
| 50.0 | |||||
| Year 2 Semester 1 | |||||
| STAT2005 | v.1 | Computer Simulation | 4.0 | 25.0 | |
| ISEC2001 | v.2 | Fundamental Concepts of Data Security | 3.0 | 25.0 | |
| SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
| 75.0 | |||||
| Year 2 Semester 2 | |||||
| STAT2003 | v.1 | Analytics for Experimental and Simulated Data | 5.0 | 25.0 | |
| COMP1002 | v.1 | Data Structures and Algorithms | 4.0 | 25.0 | |
| SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
| 75.0 | |||||
| Year 3 Full Year | |||||
| NPSC3000 | v.1 | Research, Leadership and Entrepreneurship in Science 2 | 7.0 | 50.0 | |
| 50.0 | |||||
| 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 | |
| SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
| 75.0 | |||||
| Year 3 Semester 2 | |||||
| STAT2004 | v.1 | Analytics for Observational Data | 4.0 | 25.0 | |
| COMP3009 | v.1 | Data Mining | 3.0 | 25.0 | |
| SELECT ELECTIVE UNITS TO THE TOTAL VALUE OF: | 25.0 | ||||
| 75.0 | |||||
| Year 4 Full Year | |||||
| NPSC4001 | v.1 | Advanced Science Capstone | 3.0 | 150.0 | |
| 150.0 | |||||
| Year 4 Semester 1 | |||||
| MATH4002 | v.1 | Advanced Topics in Optimisation | 4.0 | 25.0 | |
| 25.0 | |||||
| Year 4 Semester 2 | |||||
| MATH4001 | v.1 | Advanced Topics in Applied and Computational Mathematics | 3.0 | 25.0 | |
| 25.0 | |||||
| Optional Units to Select from in Year 1 Semester 2 | Hrs/Wk | Credit | |||
| COMP1000 | v.1 | Unix and C Programming | 4.0 | 25.0 | |
| CHEM1000 | v.1 | Principles and Processes in Chemistry | 7.0 | 25.0 | |
| PHYS1006 | v.1 | Foundations of Physics | 5.0 | 25.0 | |
| MATH1006 | v.1 | Mathematical Modelling | 4.0 | 25.0 | |
| ASTR1003 | v.1 | Introduction to Astronomy | 4.0 | 25.0 | |
| BIOL1000 | v.1 | Functional Biology | 5.0 | 25.0 | |
| MATH1016 | v.1 | Calculus 1 | 5.0 | 25.0 | |
| MATH1018 | v.2 | Advanced Mathematics 2 | 4.0 | 25.0 | |
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