MDDU-DATSC v.1 Data Science Double Degree Major (BSc/BA, BSc/BCom)
This major/stream is part of a larger course. Information is specific to the major/stream, please refer to the course for more information.
Data is used in all industries to drive innovation and growth. Data Scientists are able to harness the power of data to be central in these processes. A Bachelor of Science (Data Science) alongside a Bachelor of Arts or Bachelor of Commerce is a powerful combination that will enable graduates to use data to solve problems through an in-depth understanding of appropriate analytics and the field in which the solutions will be applied. Through the BSc (Data Science) Double Degree Major, students will gain a high level of computer programming competency, the capacity to apply appropriate statistical procedures to large datasets from different sources, an understanding of contemporary digital media, and the ability to communicate data-driven solutions to a range of audiences. The field of data science is truly interdisciplinary and is therefore taught by experts from an array of discipline areas including Computing, Mathematics and Statistics, Humanities and Economics.
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
4 years or equivalent part time study
|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|
|Year 1 Semester 2|
|MATH1015||v.1||Linear Algebra 1||4.0||25.0|
|STAT1002||v.1||Statistical Data Analysis||3.0||12.5|
|Year 2 Semester 1|
|ISEC2001||v.2||Fundamental Concepts of Data Security||3.0||25.0|
|STAT1000||v.1||Regression and non-Parametric Inference||3.0||12.5|
|Year 2 Semester 2|
|COMP1002||v.1||Data Structures and Algorithms||4.0||25.0|
|STAT2003||v.1||Analytics for Experimental and Simulated Data||5.0||25.0|
|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|
|Year 3 Semester 2|
|ICTE2000||v.2||Interactive, Virtual and Immersive Environments||25.0|
|STAT2004||v.1||Analytics for Observational Data||5.0||25.0|
|Year 4 Semester 1|
|MEDA3000||v.2||Mobile, Locative and Ubiquitous Media||25.0|
|CNCO3003||v.1||Mobile Cloud Computing||3.0||25.0|
|Year 4 Semester 2|
|SELECT OPTIONAL UNITS TO THE TOTAL VALUE OF:||25.0|
|Optional Units to Select from in Year 4 Semester 2||Hrs/Wk||Credit|
|COMP3005||v.1||Computer Project 2||9.0||25.0|
|MEDA3001||v.2||Major Digital Humanities Project||25.0|
|ISYS3002||v.2||Information Systems and Technology Project 2||2.0||25.0|
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