36124 Applied 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 a holistic view of navigating through data science for brining innovation into the real-life problems. Participants acquire relevant technical skills in a comprehensive manner starting from exploratory data analysis, data visualisations leading to understanding of target prediction feature category and selection of ML based on the analysis. This is followed by regression and classification analysis. While getting hands on to the core applied machine learning, students are also introduced to the agile methodology for running a controlled ML experiment. Finally, students are introduced to techniques to save the trained models and then load the trained models in production environment.

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 machinelearning algorithms, as well as to consider standards and ethical implications of their work.

Minimum requirements

Students must participate in all online sessions and complete exercises provided in lab sessions and the assessment tasks