During this course, you will meet and work with a dedicated course facilitator who will support your learning and engagement with teaching resources designed by the lead academic and team of experts from the Faculty of Engineering and IT.
The course is structured into five modules. Each module comprises self-study materials and facilitated online sessions. The five modules and key topics covered are:
Module 1 - Feature selection
- This module introduces feature selection in data pre-processing. Participants will learn to pre-process noisy and unreliable data sets to remove irrelevant or redundant information and explore feature ranking to aid data-driven decision making.
Module 2 - Hyper-parameter optimisation
- This module covers parameter optimisation models to minimise error and maximise accuracy. We will cover approaches such as brute force, hill climbing, random search and grid search.
Module 3 - Knowledge discovery process
- Participants will learn about intelligent data management and data mining project development. We will cover the process of whiteboarding data products, feature engineering and systems engineering in data mining.
Module 4 - Research landscape in data analytics
- This module introduces the current landscape in data analytics research. Key topics we will explore include the challenges of big data and big data architectures, information provenance and intelligent systems and computational intelligence.
Module 5 - Industrial applications
- In this module we will apply and evaluate the models and approaches learnt through this course, via examples and case studies from a range of sectors, including, but not limited to, healthcare, agriculture, telecommunications, education, finance and transportation.
This course is delivered in a scheduled format over ten weeks.
Each week (during weeks 1 to 8) you will participate in an online session where you will have the chance to apply what you've learned, ask questions and hear from other participants who are taking the course with you. The workshops are led by the course facilitator.
Weeks nine and ten are planned to give you time to complete the final assignment, with support and scheduled Q&A sessions provided.
This course includes weekly, live, one-hour online tutorials and one-hour weekly Q&A sessions facilitated by an expert UTS academic supporting self-study and online learning activities.
Case studies of real-world business illustrate applications of data mining techniques. The workshop sessions focus on hands-on experience in data mining and data analytics tools and understanding and interpretation of the results.
Regular formative quizzes throughout the microcredential will allow participants to gauge their progress.