Quantitative Analysis
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Prerequisites
NIT1201 - Introduction to Database Systems
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
In this unit you will be equipped with fundamental quantitative techniques essential for modern analytics. You will explore concepts such as linear algebra, calculus, probability, and optimisation, and apply them to data-driven decision-making processes for interpreting complex datasets, validating models, and improving algorithmic performance. A key focus is leveraging GenAI-powered tools to enhance problem-solving efficiency and automate data analysis tasks. By the end of this unit, you will gain the ability to critically assess quantitative models, optimise algorithms, and apply statistical reasoning in real-world contexts: preparing you for roles in data science across research and industry.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Explain the mathematical and statistical foundations of data science, including linear algebra, probability, calculus, and optimisation;
- Analyse data distributions, statistical models, and hypothesis testing to extract meaningful insights;
- Apply optimisation techniques to enhance machine learning models and improve data-driven decision-making;
- Implement quantitative analysis methods with GenAI-powered tools to automate analytical workflows;
- Evaluate the effectiveness of statistical and mathematical models in solving real-world data science problems individually and as part of a group.
Assessment
For Melbourne campuses
Assessment type: Test | Grade: 20%
Online test
Assessment type: Assignment | Grade: 40%
Applied data analysis project with Q&A verification.
Assessment type: Case Study | Grade: 40%
Scenario-based problem solving with verified live interaction.
Assignment: 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):