Predictive Analytics

Unit code: BCO6008 | Study level: Postgraduate
12
(Generally, 1 credit = 10 hours of classes and independent study.)
City Campus
Online Real Time
VU Brisbane
VU Sydney
BCO7000 - Business Analytics and Visualisation; and
BCO7006 - Coding for Business Analytics
(Or equivalent to be determined by unit coordinator)
Overview
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Overview

The unit is focused on using big data to identify future outcomes and best-assessment of future actions using predictive analytics solutions. Predictive analytics creates a critical competitive advantage for current businesses using data as their most valuable business asset. Using open source environments (R/Python) and collaborative development practices students, are exposed to algorithms and machine learning techniques to capture important trends, predict anomalies and forecast business outcomes. The unit is based upon using real-world scenarios and business data. It equips students with solid theoretical foundation of business analytics and builds practical skills.

Learning Outcomes

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

  1. Critically analyse the use of predictive analytics approaches in decision making for business organisations to deconstruct complex business problems;
  2. Design reproducible predictive analytics solutions using open-source environments (R/Python);
  3. Evaluate the selection, implementation and business use of predictive analytics solutions using open source environments (R/Python) in regards to statistical rigour and addressing business goals; and,
  4. Advocate the devised predictive analytics solutions to stakeholders and interpret outcomes to inter-disciplinary audiences.

Assessment

For Melbourne campuses

Assessment type: Test
|
Grade: 25%
Two online quizzes (10%, 15%)
Assessment type: Assignment
|
Grade: 35%
Data analytics project
Assessment type: Assignment
|
Grade: 40%
Working with a real-world data set to develop and evaluate a data model - research report (25%) presentation (15%) (Group)

Required reading

Selected readings will be made available via the unit VU Collaborate site.

As part of a course

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

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