COIT20280 - Principles of Data Analytics

General Information

Unit Synopsis

Data analytics is the science of analysing raw data to make conclusions about that information. It helps businesses optimise their performances. Data analytics techniques can reveal trends that would otherwise be lost in the mass of information. In this unit, you will study the basics of data analytics including what kinds of problems data analytics can solve. You will also learn different tools and techniques to analyse and interpret structured, unstructured and semi structured data sets. Throughout the unit, you will reflect on real-life case studies to gain an appreciation of the magnitude of the importance of data analytics in addressing the cyber security challenges in the future.

Details

Level Postgraduate
Unit Level Not Applicable
Credit Points 6
Student Contribution Band 2
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites There are no pre-requisites for the unit.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Class Timetable View Unit Timetable
Residential School No Residential School

Be Different Intakes

Note: BeDifferent Units are available to study all year round
Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

Assessment Task Weighting
1. Online Quiz(zes) 20%
2. Portfolio 80%

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University’s Grades and Results Policy for more details of interim results and final grades

Unit learning Outcomes

On successful completion of this unit, you will be able to:

  1. Compare techniques to analyse and interpret structured and unstructured data
  2. Justify and present a case for the selection of data sets for given data analytics problems
  3. Apply tools to process and visualise data sets
  4. Assess the output of the data analysis process.

The Skills Framework for the Information Age (SFIA) defines skills and competencies of ICT professionals. SFIA is used internationally in job descriptions, role profiles and to describe graduate outcomes. This unit contributes to the following workplace skills as defined by SFIA 7 (the SFIA code is included):

  • Analytics (INAN)
  • Data visualisation (VISL)
  • Data management (DATM)
  • Methods and tools (METL)
  • Programming/software development (PROG)

Alignment of Assessment Tasks to Learning Outcomes
Assessment Tasks Learning Outcomes
1 2 3 4
1 - Online Quiz(zes)
2 - Portfolio
Alignment of Graduate Attributes to Learning Outcomes
Advanced Level
Professional Level
Graduate Attributes Learning Outcomes
1 2 3 4
1 - Knowledge
2 - Communication
3 - Cognitive, technical and creative skills
Alignment of Assessment Tasks to Graduate Attributes
Advanced Level
Professional Level
Assessment Tasks Graduate Attributes
1 2 3 4 5 6 7
1 - Online Quiz(zes)
2 - Portfolio