Overview

This unit aims to develop students’ conceptual and practical understanding of the field of data analytics in the contexts of real-world applications. The students will gain the understanding of how to identify and define data science relevant tasks in practical scenarios, and acquire the ability to apply given techniques and tools using Python to resolve given data analytics relevant tasks.

Requisites

Prerequisites
COS60012 Data Analytics with Python
Teaching periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date

Learning outcomes

Students who successfully complete this unit will be able to:

  • Design machine learning models to inform organisational decision-making
  • Apply relevant algorithms to analyse data and make predictions using Python
  • Test, evaluate, improve and deploy an effective machine learning model using Python
  • Justify the approach taken to create machine learning models within different organisational contexts
  • Examine ethical considerations within specific data science contexts

Teaching methods

Swinburne Online

Type Hours per week Number of weeks Total (number of hours)
Online
Directed Online Learning and Independent Learning		15.00 10 weeks 150
TOTAL150

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Live Online
Class		1.50 8 weeks 12
Online
Learning activities		1.50 8 weeks 12
On-campus
Workshop		3.00 8 weeks 24
Unspecified Activities
Independent Learning		12.75 8 weeks 102
TOTAL150

Assessment

Type Task Weighting ULO's
Assignment 1 Individual/Group  40 - 60%  1 
Assignment 2 Individual  40 - 60%  2,3,4,5 

Hurdle

As the minimum requirements of assessment to pass the unit and meet all Unit Learning Outcomes to a minimum standard, a student must achieve:

(i) An aggregate mark of 50% or more, and
(ii) Complete both assignments.

Students who do not successfully achieve hurdle requirement (ii) will receive a maximum of 45% as the total mark for the unit.

Content

  • Introduction to data science
  • Machine learning models
  • Predictive analytics
  • Advanced analytics
  • Biases
  • Communicating insights

Study resources

Reading materials

A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.