Introduction to Data Science

Unit code: NIT1001 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Footscray Park
VU Brisbane
VU Sydney
Prerequisites
NIT1102 - Introduction to Programming
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

In this unit, you will explore how data drives decisions by learning the essentials of statistics and data visualisation. From interpreting patterns in data to building interactive dashboards with Power BI, you will apply statistical techniques to real-world problems. You will also gain experience with AI-assisted tools like GitHub Copilot to streamline your analysis workflow. By the end of the unit, you will be able to analyse and visualise data to uncover insights: preparing you for roles in analytics, business intelligence, and advanced data science study.

Learning Outcomes

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

  1. Apply descriptive and inferential statistical techniques to analyse datasets and inform data-driven decision-making in diverse contexts.
  2. Interpret and evaluate probability distributions and statistical models to identify patterns and trends in real-world datasets.
  3. Apply AI-assisted tools, including GitHub Copilot, to automate statistical analysis and enhance workflow efficiency and accuracy individually and as part of a team.
  4. Design and develop interactive data visualisations and dashboards using Power BI to effectively communicate insights for informed decision-making.

Assessment

For Melbourne campuses

Assessment type: Laboratory Work
|
Grade: 20%
In class practical test with verified live interaction.
Assessment type: Assignment
|
Grade: 40%
PowerBI hands-on assignment with progressive submission. (Group)
Assessment type: Case Study
|
Grade: 40%
Data analysis and reporting using PowerBI with Q&A verification.

Laboratory Work: Conducted under supervision, students complete real-time data analysis, coding, or modelling tasks. Execution is observed, and outcomes are verified through live checks or oral Q&A to ensure individual performance.

Case Study: Students apply concepts to real-world 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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