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Master of Data Science (Artificial Intelligence and Machine Learning)

University of Southern Queensland
  • Study the Artificial Intelligence and Machine Learning specialisation with UniSQ and explore concepts regarding deep learning, natural language processing, information retrieval and knowledge management. 
  • In an increasingly data driven world, it's critical that we can use information retrieval technologies and systems to query and retrieve useful information to support advanced data analytics and inform high-level decision-making.
  • Explore concepts and learning within big data management, machine learning and data mining to ensure you have the knowledge for a career in this dynamic industry. 
  • Choose between two different pathways of research in your masters degree:
    • Research Project track — this pathway equips you with research skills and enables you to develop a research thesis in applied data science through an independent project in your chosen disciplinary application area.
    • Research Training track — this pathway allows you to develop your research capability and gain specialised cognitive and technical computational and information system skills to prepare for a professional career in science or your chosen disciplinary application area.
  • Research Project track — this pathway equips you with research skills and enables you to develop a research thesis in applied data science through an independent project in your chosen disciplinary application area.
  • Research Training track — this pathway allows you to develop your research capability and gain specialised cognitive and technical computational and information system skills to prepare for a professional career in science or your chosen disciplinary application area.
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    Requirements

    Listed below are the documents required to apply for this course.
    2 years

    Duration

    Feb 2025

    Start Month

    Jan 2025

    Application Deadline

    Upcoming Intakes

    • July 2024
    • November 2024
    • February 2025
    • July 2025
    • November 2025

    Mode of Study

    • Full Time