CQUniversity Unit Profile

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COIT20253 Business Intelligence using Big Data
Business Intelligence using Big Data
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All details in this unit profile for COIT20253 have been officially approved by CQUniversity and represent a learning partnership between the University and you (our student).
The information will not be changed unless absolutely necessary and any change will be clearly indicated by an approved correction included in the profile.
General Information

Overview

Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data. In this unit, you will explore big data within the context of business intelligence. In this unit, you will learn concepts of business intelligence, alignment of big data to business intelligence and how big data technologies can be used in building organisational business intelligence. You will learn how big data is changing businesses and how organisations can take advantage of big data in decision making. You will learn how organisations are integrating non-traditional unstructured data with the traditional structured enterprise data to do the business intelligence analysis. In order to understand these, you will learn big data analytical tools and technologies to help solve authentic business problems and make effective business decisions.

Details

Career Level: Postgraduate
Unit Level: Level 9
Credit Points: 6
Student Contribution Band: 8
Fraction of Full-Time Student Load: 0.125

Pre-requisites or Co-requisites

Prerequisites: COIT20250 e-Business Systems, COIT20245 Introduction to Programming and COIT20247 Database Design and Development. Anti-Requisites: If you have completed unit COIT20236 then you cannot take this unit.

Important note: Students enrolled in a subsequent unit who failed their pre-requisite unit, should drop the subsequent unit before the census date or within 10 working days of Fail grade notification. Students who do not drop the unit in this timeframe cannot later drop the unit without academic and financial liability. See details in the Assessment Policy and Procedure (Higher Education Coursework).

Offerings For Term 1 - 2024

Brisbane
Melbourne
Online
Rockhampton
Sydney

Attendance Requirements

All on-campus students are expected to attend scheduled classes – in some units, these classes are identified as a mandatory (pass/fail) component and attendance is compulsory. International students, on a student visa, must maintain a full time study load and meet both attendance and academic progress requirements in each study period (satisfactory attendance for International students is defined as maintaining at least an 80% attendance record).

Website

This unit has a website, within the Moodle system, which is available two weeks before the start of term. It is important that you visit your Moodle site throughout the term. Please visit Moodle for more information.

Class and Assessment Overview

Information for Class and Assessment Overview has not been released yet.

This information will be available on Monday 8 January 2024
CQUniversity Policies

All University policies are available on the CQUniversity Policy site.

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This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.

Previous Student Feedback

Feedback, Recommendations and Responses

Every unit is reviewed for enhancement each year. At the most recent review, the following staff and student feedback items were identified and recommendations were made.

Feedback from Student evaluation

Feedback

Students were keen to learn new tools, especially tools for data visualisuation and text analysis which are growing areas in business intelligence

Recommendation

Provide additional resources on tools to support student learning and enhance their skills

Feedback from Self and teaching team evaluation

Feedback

Inadequate real-life examples of Big Data business opportunities in Industry 4.0

Recommendation

Provide more real-life examples of big data technologies to enhance students’ understanding of the application of Big Data in Industry 4.0

Unit Learning Outcomes

Information for Unit Learning Outcomes has not been released yet.

This information will be available on Monday 8 January 2024
Alignment of Learning Outcomes, Assessment and Graduate Attributes

Information for Alignment of Learning Outcomes, Assessment and Graduate Attributes has not been released yet.

This information will be available on Monday 8 January 2024
Textbooks and Resources

Information for Textbooks and Resources has not been released yet.

This information will be available on Monday 12 February 2024
Academic Integrity Statement

Information for Academic Integrity Statement has not been released yet.

This unit profile has not yet been finalised.