36125 Advanced Data Science for Innovation
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 2 cp
Result type: Grade, no marks
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.