Predictive Analysis

Unit code: NIT3151 | Study level: Undergraduate
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Footscray Park
NIT2151 - Fundamentals of Data Science; and
NIT2251 - Machine Learning and Data Mining
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

Predictive Analytics is a unit that encompasses the core body of knowledge in data science. Students will learn to analyse business needs and provide predictive solutions through team work and communication. Students will develop knowledge of the fundamental processes and methods in data science and their application in predictive analytics, including data exploration; classification and clustering; time series; model validation and deployment. Further, students will test and validate their models so that they can develop appropriate predictive methods to solve real-world problems.

Learning Outcomes

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

  1. Apply theories and methods in predictive analytics;
  2. Analyse, explore, and process real-world data sets;
  3. Adapt modern ethical and professional practice in data analytics;
  4. Propose solutions in predictive analysis by utilising state-of-the-art software and frameworks; and
  5. Present visualisations of data that communicates results of analysis to a wider audience.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 20%
Online test
Assessment type: Project
|
Grade: 40%
Group project based assessment
Assessment type: Test
|
Grade: 20%
Scenario-based in-class problem solving
Assessment type: Presentation
|
Grade: 20%
Individual presentation on group project

Required reading

Required and recommended readings will be made available on VU Collaborate.

As part of a course

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

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