Advanced Data Science

Unit code: NIT3251 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Footscray Park
Prerequisites
NIT2002 - Data Science Methods and Applications; or
NIT2251 - Machine Learning and Data Mining
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

In this unit, you will develop a deep understanding of data exploration, visualisation, analysis, and advanced machine learning/deep learning techniques. You will gain hands-on experience with open-ended investigations of real-world practical dataset and novel data science problems. You will gain better understanding of the characteristics of different algorithms, tools, frameworks, and technologies. You will apply, evaluate, and develop advanced models like CNN, LSTM, GNN, GAN, Transformer, LangChain, etc. By the end of the unit, you will be able to design data-driven solutions that support decision-making in roles such as AI development, research analytics, and applied machine learning.

Learning Outcomes

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

  1. Design advanced machine learning and deep learning applications to solve complex data science problems;
  2. Evaluate diverse data science methodologies across various applications;
  3. Design data-driven solutions with optimised performance and appropriate model interpretability individually and as part of a team;
  4. Integrate ethical, legal, and social considerations into responsible data-driven decision-making;
  5. Exhibit complex analytical insights effectively to diverse audiences, supporting business and research-driven applications.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 20%
Open test
Assessment type: Project
|
Grade: 40%
Applied problem solving - code, and report with demonstration and oral Q&A (Group)
Assessment type: Case Study
|
Grade: 40%
Case study and algorithm implementation - code, and report 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 Q&A 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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