Overview

This unit aims to develop students’ conceptual and practical understanding of the field of data science in the contexts of real-world applications. The students will learn about basic concepts, key techniques and popular tools in various aspects of data science, following the lifecycle of a practical data science project which involves data collection, management, wrangling, analytics and visualisation. They 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 to resolve given data science relevant tasks.

Requisites

Prerequisites
COS60008 Introduction to Data Science

Rules

MA-ITPC1 Master of Information Technology (Professional Computing)
OR
COS60006 Introduction to Programming
OR
Concurrent Pre-requisite
COS60010 Technology Inquiry Project

Assumed Knowledge
Basic understanding of Database concepts

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:

  • Demonstrate knowledge of fundamental concepts, key techniques and popular tools in data science
  • Demonstrate understanding of the lifecycle of a practical data science project
  • Demonstrate how to identify and define data science relevant tasks in practical scenarios
  • Critically analyse, evaluate and apply given techniques and tools to solve given data science relevant problems

Teaching methods

Hawthorn

Type Hours per week Number of weeks Total (number of hours)
Online
Lecture
1.00 12 weejk 12
On-campus
Lecture
1.00  12 weeks  12
On-campus
Lab
2.00  12 weeks  24
Unspecified Activities
Independent Learning
8.50  12 weeks  102
TOTAL     150

Assessment

Type Task Weighting ULO's
Assignment Individual  30 - 50%  1,2,3 
Project Individual/Group  40 - 60%  1,2,3,4 
Test Individual  10 - 30%  1,2,3,4 

Content

  • Foundations of data science
  • Data science in real world
  • Lifecycle of practical data science projects
  • Data collection: basic concepts, techniques and tools
  • Data management: basic concepts, techniques and tools
  • Data wrangling: basic concepts, techniques and tools
  • Data analytics: basic concepts, techniques and tools
  • Data visualisation: basic concepts, techniques and tools
  • Applications of given data science relevant techniques and tools to deal with specific data science relevant tasks

Study resources

Reading materials

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