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SIT741 - Statistical Data Analysis

Year:

2020 unit information

Important Update:

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

Enrolment modes:Trimester 2: Burwood (Melbourne), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 2: Sergiy Shelyag
Prerequisite:

MIS770 and SIT718

For students enrolled in S577: Nil

Corequisite:

Nil

Incompatible with:

Nil

Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus:

1 x 2 hour class per week, 1 x 1 hour practical per week.

Scheduled learning activities - cloud:

1 x 1 hour scheduled online workshop per week.

Content

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

ULO1

Apply statistical thinking to analyse problems of real-world complexity and formulate corresponding statistical inference questions.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving

ULO2

Demonstrate in-depth knowledge in advanced statistical models including generalized linear models and generalized additive models.

GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO4: Critical thinking

ULO3

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 capabilities
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving
GLO6: Self-management

ULO4

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 capabilities
GLO2: Communication
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving

ULO5

Apply statistical thinking to analyse problems of real-world complexity and formulate corresponding statistical inference questions.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving

These Unit Learning Outcomes are applicable for all teaching periods throughout the year

Assessment

Assessment Description Student output Weighting (% total mark for unit) Indicative due week
Problem solving tasks

Two problem solving tasks.

Research, case analysis, evaluations and presentations

65% (25% + 40%) Weeks 6 and 10
Examination 2-hour written examination 35% Examination period

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.

Learning Resource

There is no prescribed text. Unit materials are provided via the unit site. This includes unit topic readings and references to further information.

Unit Fee Information

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