2020 unit information
Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.
Last updated: 2 June 2020
MIS770 and SIT718
For students enrolled in S577: Nil
Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.
1 x 2 hour class per week, 1 x 1 hour practical per week.
1 x 1 hour scheduled online workshop per week.
The aim of this unit is to provide students with the opportunity to develop advanced working knowledge in statistical modelling and statistical programming. Students will learn how to apply advanced statistical theories such as generalised additive modelling to model real-world data problems. They will also learn about advanced statistical programming using the R language, to perform simulation, model development, model checking, and result interpretation.
Upon successful completion of this unit, students will be able to apply the right statistical models, including generalised linear models and generalised additive models, to solve problems of real-world complexity. They will know how to use R to transform untidy data to tidy data, to perform exploratory data analysis, to develop and check models, and to communicate the analysis results.
These are the Learning Outcomes (ULO) for this Unit
At the completion of this unit successful students can:
Deakin Graduate Learning Outcomes
Apply statistical thinking to analyse problems of real-world complexity and formulate corresponding statistical inference questions.
GLO1: Discipline-specific knowledge and capabilitiesGLO4: Critical thinkingGLO5: Problem solving
Demonstrate in-depth knowledge in advanced statistical models including generalized linear models and generalized additive models.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO4: Critical thinking
Apply the statistical language R to transform untidy data into tidy data, to perform exploratory data analysis, and to develop and check models.
GLO1: Discipline-specific knowledge and capabilitiesGLO3: Digital literacyGLO4: Critical thinkingGLO5: Problem solvingGLO6: Self-management
Apply modern statistical computing tools to report analyses to the wider communities, with effective use of advanced interactive information visualisation methods.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO3: Digital literacyGLO4: Critical thinkingGLO5: Problem solving
These Unit Learning Outcomes are applicable for all teaching periods throughout the year
Two problem solving tasks.
Research, case analysis, evaluations and presentations
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.
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