Big Data and Machine Learning in Business

Unit code: BCO2009 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
City Campus
VU Brisbane
Prerequisites
N/A
Overview
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Overview

Machine learning equips managers to use a platform that becomes intelligent. Machine Learning using large data sets is rapidly changing businesses and transforming the future workplace by enhancing business capability and improving business operations.



In this unit, you will develop knowledge and skills to understand concepts and the conceptual framework underpinning structured and unstructured data. use big data for machine learning to:


  • to automate transactions
  • help businesses predict revenue
  • analyse cost patterns for businesses
  • detect potential frauds
  • perform financial analysis

 After completing the unit, you will develop skills to apply technical elements of machine learning in a business environment, which will equip you with the conceptual and technical knowledge to automate and improve decision-making for business organisations.

Learning Outcomes

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

  1. Critically review the selection and automation of data processing approaches and solutions;
  2. Recommend solutions to business problems through the application of data mining tools;
  3. Evaluate and implement machine learning solutions for business organisations;
  4. Create and develop machine learning models for the implementation in businesses; and,
  5. Analyse and recommend applications of machine learning using big data.

Assessment

For Melbourne campuses

Assessment type: Exercise
|
Grade: 20%
Problem-based machine learning exercises
Assessment type: Project
|
Grade: 40%
Project on enhancing the capabilities of the decision-making system using machine learning with large data sets (Group)
Assessment type: Report
|
Grade: 40%
Scenario based analysis and development of machine learning models

Required reading

Readings and resources are available via VU Collaborate.

As part of a course

This unit is not compulsory for any specific course. Depending on the course you study, this unit may be taken as an elective.

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