Data Analytics for ICT Business

Unit code: NIT3007 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Footscray Park
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
NIT2202 - Big Data
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

In this unit, you will learn to explore data analytics in ICT business contexts, covering business intelligence, customer analytics, and AI-driven insights for strategic decision-making. You will apply advanced analytical techniques and visualisation tools to real-world problems, working in teams on hands-on projects. The unit also examines ethical, legal, and regulatory issues in data use. By the end, you will be equipped to evaluate analytics tools, generate actionable insights, and deliver data-driven recommendations: preparing you for roles in business analytics, data strategy, or further studies in enterprise intelligence.

Learning Outcomes

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

  1. Determine data analytics methodologies, including AI-driven techniques, to solve complex business challenges in ICT enterprises;
  2. Critically evaluate data mining models, business intelligence tools, and visualisation techniques to support strategic decision-making;
  3. Develop data-driven business reports that incorporate actionable insights and recommendations for ICT business problems individually and as part of a team;
  4. Assess the ethical, legal, and regulatory considerations in business analytics, ensuring responsible data use in decision-making;
  5. Report and justify analytical insights using data visualisation and narrative techniques for business stakeholders.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 20%
Online test
Assessment type: Project
|
Grade: 35%
Business analytics project (Group) with demonstration, presentation and oral Q&A.
Assessment type: Case Study
|
Grade: 45%
Scenario-based data analytics and visualisation with Q&A verification.

Project: Includes structured milestone reviews and in-class progress checks. Individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.

Case Study: Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.

Required reading

Selected readings are provided on VU Collaborate.

As part of a course

This unit is studied as part of the following course(s):

Bachelor of Data Science
NBDS | Bachelor degree | Duration: 3 years

Refer to the course page for information on how to apply for the course. VU takes care to ensure the accuracy of this unit information, but reserves the right to change or withdraw courses offered at any time. Please check that unit information is current with the Student Contact Centre.

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