Data Science Methods and Applications
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Prerequisites
NIT1102 - Introduction to Programming
(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 learn key methods for data collection, cleaning, analysis, visualisation, and interpretation, while considering ethical issues, data privacy laws, and regulatory compliance. You will work hands-on with Python and AI-assisted tools like GitHub Copilot to automate tasks and improve efficiency. You are expected to apply industry-standard tools to manipulate, preprocess, and model data through real-world case studies. By the end of the unit, you can confidently use AI tools to enhance your data analytics workflow, preparing you for further study and careers in data science and analytics.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Exhibit the core principles, workflows, and ethical considerations in the data science process.
- Apply data wrangling techniques to collect, clean, and prepare raw data for analysis.
- Analyse datasets using exploratory techniques to identify patterns and generate insights.
- Interpret statistical properties of data using appropriate probability distributions and hypothesis testing methods.
- Collaborate in integrating AI-assisted tools into data science workflows to improve productivity and present findings.
Assessment
For Melbourne campuses
Assessment type: Test | Grade: 20%
Open book online test
Assessment type: Project | Grade: 40%
Business-oriented analysis project (Group) with demonstration, presentation and oral Q&A.
Assessment type: Case Study | Grade: 40%
Scenario-based in-class problem solving with verified live interaction.
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):