Business Intelligence

Subject BUSA90493 (2014)

Note: This is an archived Handbook entry from 2014.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2014:

Term 2, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable

Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 30 hours
Total Time Commitment: Not available
Prerequisites: None
Corequisites: None
Recommended Background Knowledge: None
Non Allowed Subjects: None
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.

It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support:

Subject Overview:

The aim of business intelligence is to teach students how to extract relevant information from large amounts of data to make improved business decisions.

Big data is big business, but there are not enough managers who can ask the right questions and interpret the results of analysis effectively. The McKinsey report predicts a need for an additional 1.5 million adequately trained managers in the US alone. Data-Mining is the science of extracting useful information from large datasets. It is the process of quantifying the dependency between outcomes and using that knowledge to predict future outcomes. Data-mining is used in all business related fields.

Learning Outcomes:

On completion of this subject the student should be able to:

  • Demonstrate how to quantitatively analyse large datasets and convert raw data into relevant information for management decisions;
  • Determine which techniques to apply to different types of data;
  • Develop presentation skills to convey this information to a non-technical audience;
  • Understand the difference between fitting observed data and making predictions regarding unseen observations.
  • Contribution to class learning (10%)
    • Throughout subject
  • 3 Syndicate assignments (30%)
    • Throughout subject
  • 8 Quizzes (40%)
    • Throughout the term
  • Syndicate presentation (10%)
    • End of subject
  • In-class Kaggle competition (10%)
    • End of subject
Prescribed Texts: None
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Related Course(s): Master of Business Administration
Master of Business Administration

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