Overview
To view overview information, please select an offering from the drop-down menu above.
Academic contacts
To view unit coordinator information, please select an offering from the drop-down menu above.
Offerings
MURDOCH-S1-INT-2018-ONGOING
Requisites
Prerequisite
Other learning activities
To view other learning activity information, please select an offering from the drop-down menu above.
Learning activities
To view learning activity information, please select an offering from the drop-down menu above.
Learning outcomes
1.
Be able to demonstrate an in-depth theoretical and practical understanding of advanced computational and statistical tools and methods for advanced data analysis.
2.
Be able to evaluate and apply machine learning and statistical analysis methods in real-life applications.
3.
Demonstrate and articulate a critical understanding of the latest approaches, theories, and research activities in data science.
4.
Be able to demonstrate knowledge of using the Python programming language and software environment for advanced data analysis.
Assessments
To view assessment information, please select an offering from the drop-down menu above.
Additional information
Unit content:· Data harvesting and wrangling
· Data visualizsation and exploration
· Big data statistics
· Inference and prediction with applications using real-life datasets
· Interactive data analysis, with application to real-life datasets
· Knowledge representation and reasoning with big data.
Other notes:Each student is expected to spend on average three hours per teaching week reading the lecture notes, books chapters and other recommended materials relevant to the topic covered in that week and spend a similar amount of time working on the workshop exercises for that week. In addition, each student is required to complete an assignment and sit the final examination.