|Year of offer||2019|
|Subject level||Graduate coursework|
Term 4 - Online
|Fees||Subject EFTSL, Level, Discipline & Census Date|
The foundational principles and practice of modern data analytics, including skills in data manipulation, presentation, and analysis; introduction to probability models used for a continuous response. Students will learn how to use methods such as linear models and tree-based methods for forecasting. Students will use statistical software to analyse data. However, emphasis will focus on interpretation and understanding of the appropriate use of data rather than the technical details of performing the analysis.
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
- List the important types of analytics software 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 Apply sound principles to create the high-level design of an analytics investigation.
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.