Outline
Outline
Data scientists work at the interface of computing, statistics, visualisation and media to collate and analyse large volumes of data and then communicate findings to a range of audiences. Their ability to leverage big data to predict future trends is becoming an essential part of decision-making in business and government.
This course sits within the double degree combination of Bachelor of Science/Bachelor of Arts, and also the Bachelor of Science/Bachelor of Commerce. Applicants have the choice of which double degree combination they would like to study.
Please refer to the handbook for additional course overview information.
What jobs can the Data Science course lead to?
- Marketing and advertising data analyst
- Pricing or financial analyst
- Game designer
- Health and allied health data analyst
- Business intelligence analyst
- Machine learning specialist
- Information security technologist
- Growth analyst
- IT statistician
What you'll learn
- understand the theoretical background to processes for efficient collection, management, secure storage and analysis of large data sets
- formulate hypotheses about data and develop innovative strategies for testing them by 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
- communicate approaches and solutions to data science problems to a range of audiences in a variety of modes
- identify, select and use appropriate open source and proprietary data management and analysis tools to identify patterns or relationships in large volumes of data
- recognise the importance of continuous learning opportunities in a rapidly developing field and engage in self-driven development as a data scientist
- understand the global nature of data science and apply appropriate international standards in data science and data analytics
- work collaboratively and respectfully with data scientists from a range of cultural backgrounds
- work professionally and ethically on independent data science projects and as a team member working collaboratively to innovative data science solutions
