36125 Advanced Data Science for Innovation

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

Note

This subject is only available via UTS open as microcredential subjects and is not suitable for enrolled MDSI students.

Description

The subject provides students an overall view of steering innovation using Machine Learning to solve the business problem in every ay life. Students acquire knowledge and skills to build various types of classification models such as Decision Trees, Random Forest, Kmean, SVM. Additionally, they also learn how to optimize the models. Finally, they are exposed to deploy machine learning algorithms in production environment using Agile methodology.

Teaching and learning strategies

Starting from an elemental perspective on data and data science, students will approach learning from their specific professional and potential future contexts. As the subject progresses, the students will be able to combine their analytical and technical skills in developing and applying various machine- learning algorithms, as well as to consider standards and ethical implications of their work.

Minimum requirements

Students must participate in all online and face to face requirements, as well as complete assessment tasks.