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COIT20253 - Business Intelligence using Big Data

General Information

Unit Synopsis

Big data is a popular term used to describe the exponential growth and availability of structured and unstructured data and business intelligence involves collecting, processing, analysing, and visualising data to help organisations make informed business decisions. In this unit, you will learn concepts of business intelligence, the alignment of big data with business intelligence, and how big data technologies can be leveraged to build organisational business intelligence. You will also explore contemporary tools in business intelligence and gain an understanding of data ethics, ensuring that data-driven solutions are developed and implemented responsibly and transparently. You will learn how to use big data for decision-making and impacting change in organisations. To understand these, you will be introduced to big data analytical tools and technologies to help solve authentic business problems and make effective business decisions. This unit provides a comprehensive foundation in big data and business intelligence with a strong business focus, equipping you with the skills needed for a successful career in data analytics along with expertise in big data strategy, architecture, and data ethics.

Details

Level Postgraduate
Unit Level 9
Credit Points 6
Student Contribution Band SCA Band 2
Fraction of Full-Time Student Load 0.125
Pre-requisites or Co-requisites

Prerequisites: COIT20250 Technologies in Information Systems Practice, and COIT20245 Introduction to Programming, and COIT20247 Database Design and Development.

Anti-Requisites: COIT20236 Business Intelligence Management 

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).

Class Timetable View Unit Timetable
Residential School No Residential School

Unit Availabilities from Term 1 - 2025

Term 1 - 2025 Profile
Brisbane
Melbourne
Online
Sydney
Term 2 - 2025 Profile
Brisbane
Melbourne
Online
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).

Assessment Overview

Recommended Student Time Commitment

Each 6-credit Postgraduate unit at CQUniversity requires an overall time commitment of an average of 12.5 hours of study per week, making a total of 150 hours for the unit.

Assessment Tasks

This information will not be available until 8 weeks before term.
To see assessment details from an earlier availability, please search via a previous term.

This is a graded unit: your overall grade will be calculated from the marks or grades for each assessment task, based on the relative weightings shown in the table above. You must obtain an overall mark for the unit of at least 50%, or an overall grade of ‘pass’ in order to pass the unit. If any ‘pass/fail’ tasks are shown in the table above they must also be completed successfully (‘pass’ grade). You must also meet any minimum mark requirements specified for a particular assessment task, as detailed in the ‘assessment task’ section (note that in some instances, the minimum mark for a task may be greater than 50%).

Consult the University's Grades and Results Policy for more details of interim results and final grades

Past Exams

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Previous Feedback

Term 1 - 2024 : The overall satisfaction for students in the last offering of this course was 96.00% (`Agree` and `Strongly Agree` responses), based on a 53.19% response rate.

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.

Source: Student Unit Teaching Evaluation
Feedback
Link content to real world applications.
Recommendation
Invite guest speakers and industry experts to share their experiences and insights into how big data and business intelligence are applied in their respective fields.
Action Taken
Guest speakers were invited in Week 7.
Source: Student Unit Teaching Evaluation
Feedback
Use more examples or elaboration.
Recommendation
Include more practical cases of how big data and business intelligence are used in various industries (e.g. healthcare, finance, retail, manufacturing) in the learning resources.
Action Taken
Practical examples and case studies shared through the announcement "Case studies shared: Application and Data Workflow Orchestration for the Modern Organization"
Source: Student Unit and Teaching Evaluation
Feedback
Most students rated the unit as Exceptional.
Recommendation
To continue with the good practices.
Action Taken
In Progress
Source: ICT Course Committee
Feedback
Aligning the unit with the latest SFIA 9 released.
Recommendation
To identify and integrate specific SFIA 9 skill categories relevant to the unit.
Action Taken
In Progress
Source: Classroom Feedback
Feedback
More hands-on exercises.
Recommendation
To add tutorial exercises based on the Spark ecosystem running in Google Colab, by referencing resources such as https://praxis-qr.github.io/BDSN/ ;https://colab.research.google.com/github/pnavaro/big-data/.
Action Taken
In Progress
Unit learning Outcomes
This information will not be available until 8 weeks before term.
To see Learning Outcomes from an earlier availability, please search via a previous term.