Data Science (Advanced)

Overview

Overview

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 and 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.

This major is part of the Bachelor of Advanced Science (Honours), a course designed for high-performing students to pursue their interest in Science through a core of research, leadership and entrepreneurship. The flexible and personalised approach to studying data science enables you to explore the field through for-credit immersive research experiences, industry placement and/or interdisciplinary team-based projects.

In your capstone experience you'll have the opportunity to pursue data science projects based anywhere from pure research through to translational (entrepreneurial) science.

The Bachelor of Advanced Science (Honours) provides opportunities through second and third year to undertake internal and external internships and immersive work experience (sourced by the student), which can be used for course credit.

You'll gain practical experience programming in both R and Python and exposure to data science professionals. Work-based learning is ensured through the requirement to engage in immersive industry and/or research experience.

Foundational studies in programming and statistics form 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.

See our handbook for additional course overview information. 

How this course will make you industry ready

This course has been developed in collaboration with industry. Data science is a dynamic field, and the course is reviewed regularly by external advisors to ensure that the course's skills and knowledge content are up-to-date and industry-relevant.

You'll have opportunities to undertake internal and external internships and immersive work experience, which can be used for course credit.

What you'll learn

• 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.
• 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.
• 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.
• Communicate approaches, ideas, findings and solutions to data science problems in a variety of modes to informed professional audiences.
• 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.
• Demonstrate intellectual independence and engage in self-driven continuous discipline and professional education and training as a data scientist.
• 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.
• 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.
• 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.