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Foundations of Analytics (MAST90135)
Graduate courseworkPoints: 12.5Online
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Term 4
Melbourne School of Professional and Continuing Education
Further information: https://study.unimelb.edu.au/find/courses/graduate/master-of-applied-analytics
Email: continuing-education@unimelb.edu.au
Phone: + 61 3 8344 0149
Monday to Friday 8am to 9pm AEST/AEDT. Weekends and University of Melbourne observed Public Holidays 10am to 5pm AEST/AEDT.
Overview
Availability | Term 4 - Online |
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Fees | Look up fees |
The Foundations of Analytics is a core subject in the Health specialisation. It looks at the foundational principles and practice of modern data analytics, including techniques of data manipulation, presentation, and analysis. It’s teaching approach will emphasis interpretation underpinned by an understanding of the appropriate use of data, rather than just disseminating the technical details about performing an analysis. Students will be introduced to probability models used for a continuous response and will learn how to use methods such as linear models, tree-based methods and forecasting. Students will also use statistical software to analyse data.
Intended learning outcomes
On completion of this subject, students will be able to:
- Apply basic methods to organise a data set for analysis, including importing into statistical software, cleaning, coding and arranging data
- List the important types of analytics software and use at least two different software packages to perform some basic analyses
- Expand on the graphics material in "Critical Thinking with Analytics" to develop more sophisticated graphics, including panel graphs
- Develop a taxonomy of graphical forms and apply it to different graphical requirements
- Assess and critique modern data visualisations
- Identify and describe several of the main forms of probability models used for description, analysis and forecasting of data, including simple analyses and models, the general linear model, models in forecasting and tree-based models
- Apply simulation methods to address a non-standard analytics problem
- Evaluate and critique the use of analytics in case studies and apply sound principles to create a high-level design for an analytics investigation
Generic skills
Students will be provided with the opportunity to practise and reinforce:
- High level written communication skills.
- Interpretation skills.
- Demonstrate competence in critical and theoretical thinking through essay writing and online discussions.
Last updated: 3 November 2022