SIT718 - Real World Analytics

Unit details

Year:

2021 unit information

Important Update:

Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered.

For Semester 1/Trimester 1 2021, teaching will be delivered in line with the most current COVIDSafe health guidelines.  This may include a mix of online and on-campus activities.  Please check your unit sites for announcements and updates one week prior to the start of Semester/Trimester.

Thank you for your flexibility and commitment to studying with Deakin in 2021.

Last updated: 16 November 2020

Enrolment modes:Trimester 1: Burwood (Melbourne), Cloud (online)
Trimester 2: Burwood (Melbourne), Cloud (online)
Trimester 3: Burwood (Melbourne), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 1: Ye Zhu
Trimester 2: Ye Zhu
Trimester 3: Maia Angelova Turkedjieva
Prerequisite:

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 2 hour workshop per week.

Scheduled learning activities - cloud:

1 x 1 hour scheduled online seminar per week.

Note:

Enrolments after commencement of trimester is subject to Unit Chair approval.

 

Content

This unit introduces students to two concepts at the heart of real world analytics: optimisation and multivariate data aggregation. Students will learn how decision-making problems in industry, business, and civic services can be solved using modern modelling and solution techniques. Students will learn how to make better decisions through mathematical methods in optimisation problems such as: production planning, time-tabling management, human resource rostering, sports program scheduling, robotics/vehicle routing, network design, and resource allocation. On the topic of aggregation, students will learn how to apply the concepts of multivariate functions in order to summarise datasets that involve several interrelated variables. They will be able to reasonably analyse datasets by interpreting the parameters associated with commonly used multivariate functions.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
ULO1

Apply knowledge of multivariate functions
data transformations and data distributions to summarise data sets.

GLO1: Discipline-specific knowledge and capabilities
GLO5: Problem solving

ULO2

Analyse datasets by interpreting summary statistics, model and function parameters.

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

ULO3

Apply game theory, and linear programming skills and models, to make optimal decisions.

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

ULO4

Develop software codes to solve computational problems for real world analytics.

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

ULO5

Demonstrate professional ethics and responsibility for working with real world data.

GLO8: Global citizenship

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
Online quizzes  Five online quizzes 20% (5 x 4%) Weeks 5, 7, 8, 10 and 12
Problem-solving tasks  Two problem solving tasks  50% (20%, 30%)  Weeks 5 and 9
Examination 2-hour written examination 30% 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

The texts and reading list for the unit can be found on the University Library via the link below: SIT718 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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