Overview
This unit introduces you to the world of data analytics in business. Business analytics uses software tools to produce strategic analyses of huge volumes of data stored in databases and data warehouses to support improved decision making. Business analytics is used in industry and government for basic reporting and descriptive analyses. Advanced predictive and prescriptive analytics also allow powerful insights to be generated. Some areas of application include improved understanding of customer behaviour, gauging sentiment on social media, analysis and prediction of factors influencing profitability and portfolio optimisation. This unit will provide you with foundation knowledge to contribute to the use of data analytics in business.
Details
Pre-requisites or Co-requisites
Students enrolling in this unit must be undertaking the CL84 Master of Business Administration (International) or the CM45 Professional Certificate in Business (Data Science).
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 - 2026
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).
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.
Class Timetable
Assessment Overview
Assessment Grading
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.
All University policies are available on the CQUniversity Policy site.
You may wish to view these policies:
- Grades and Results Policy
- Assessment Policy and Procedure (Higher Education Coursework)
- Review of Grade Procedure
- Student Academic Integrity Policy and Procedure
- Monitoring Academic Progress (MAP) Policy and Procedure - Domestic Students
- Monitoring Academic Progress (MAP) Policy and Procedure - International Students
- Student Refund and Credit Balance Policy and Procedure
- Student Feedback - Compliments and Complaints Policy and Procedure
- Information and Communications Technology Acceptable Use Policy and Procedure
This list is not an exhaustive list of all University policies. The full list of University policies are available on the CQUniversity Policy site.
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 Self-reflection
Practical application of Moodle materials
The unit contents, including Moodle material, need to have a more practical application to real-world examples and cases
- Critically evaluate the role of business analytics techniques in improving decision making for a data-driven organisation
- Analyse how specific business analytics techniques influence critical success factors
- Apply descriptive business analytics techniques to assist managers to solve business problems
- Apply predictive and prescriptive business analytics techniques to assist managers to solve business problems.
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Presentation - 25% | ||||
| 2 - Project (applied) - 35% | ||||
| 3 - Project (applied) - 40% | ||||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | |||
|---|---|---|---|---|
| 1 | 2 | 3 | 4 | |
| 1 - Knowledge | ||||
| 2 - Communication | ||||
| 3 - Cognitive, technical and creative skills | ||||
| 4 - Research | ||||
| 5 - Self-management | ||||
| 6 - Ethical and Professional Responsibility | ||||
| 7 - Leadership | ||||
| 8 - First Nations Knowledges | ||||
| 9 - Aboriginal and Torres Strait Islander Cultures | ||||
Textbooks
Business Analytics
- 5th edition (2024)
- Authors: Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann
- Cengage Learning
- Boston Boston , MA , United State of America
- ISBN: 978-0-357-90220-2
- Binding: Paperback
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- Excel 2016 with Add-in Solver
- Excel 2016 (onwards) with Data Analysis Toolpak
- Excel spreadsheet software
- Zoom (both microphone and webcam capability)
All submissions for this unit must use the referencing style: American Psychological Association 7th Edition (APA 7th edition)
For further information, see the Assessment Tasks.
s.kuik@cqu.edu.au
Week 1 Introduction to Business Analytics
Begin Date: 09 Mar 2026Module/Topic
Introduction to Business Analytics
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 2 Descriptive Statistics
Begin Date: 16 Mar 2026Module/Topic
Descriptive Statistics
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 3: Data Visualisation
Begin Date: 23 Mar 2026Module/Topic
Data Visualisation
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 4: Basic Relationships of Probability
Begin Date: 30 Mar 2026Module/Topic
Basic Relationships of Probability
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 5: Analysis of Probability Distributions
Begin Date: 06 Apr 2026Module/Topic
Analysis of Probability Distributions
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 6: Statistical Inference
Begin Date: 13 Apr 2026Module/Topic
Statistical Inference
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Vacation Week
Begin Date: 20 Apr 2026Module/Topic
Chapter
Events and Submissions/Topic
Week 7: Understanding of Hypothesis Testing
Begin Date: 27 Apr 2026Module/Topic
Understanding of Hypothesis Testing
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 8: Linear Regression Analysis
Begin Date: 04 May 2026Module/Topic
Linear Regression Analysis
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 9: Decision Analytics
Begin Date: 11 May 2026Module/Topic
Decision Analytics
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 10: Optimisation Modelling
Begin Date: 18 May 2026Module/Topic
Optimisation Modelling
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 11: Forecasting Analysis
Begin Date: 25 May 2026Module/Topic
Forecasting Analysis
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Week 12: Business Analytics in Practice
Begin Date: 01 Jun 2026Module/Topic
Business Analytics in Practice
Chapter
Lecture notes and materials are available in Moodle.
Events and Submissions/Topic
Exam Week
Begin Date: 08 Jun 2026Module/Topic
Chapter
Events and Submissions/Topic
Vacation/Exam Week
Begin Date: 15 Jun 2026Module/Topic
Chapter
Events and Submissions/Topic
1 Presentation
Assessment 1 requires students to adhere to the guidelines on the use of artificial intelligence tools as specified in the Artificial Intelligence Assessment Scale (AIAS). Any misuse or lack of disclosure regarding the use of AI tools will be considered a breach of academic integrity.
Assessment 1 must be completed at AIAS Level 1. You must not use AI at any point to complete the assessment task.
You are required to prepare and deliver a 10-minute oral presentation on a data analytics case application. The case application should demonstrate your ability to analyse a dataset, extract meaningful insights, and present your findings in a clear and engaging manner. This assessment aims to evaluate your technical understanding of data analytics, your ability to interpret and visualize data, and your presentation skills. The case application and information will be provided on the unit website.
Week 5 Friday (10 Apr 2026) 11:45 pm AEST
Week 6 Friday (17 Apr 2026)
Grades and feedback comments are released in Moodle. Feedback Studio and the Grade book are the designated platforms for reviewing outcomes from the assessment process
Your oral presentation will be assessed according to the following criteria.
- Demonstrates critical thinking by providing insightful data analysis and innovative perspectives: 20%
- Demonstrates understanding of utilising software and solving numerical problems: 15%
- Demonstrates knowledge and understanding of model content, integrating key concepts: 15%
- Uses a clear outline, e.g., transitions smoothly between sections; logical flow supports comprehension: 10%
- Employs effective slides that enhance understanding, such as clear visuals, minimal clutter, consistent style: 10%
- Stays within allocated time and maintain engagement: 10%
- Exhibits communication and presentation skills with clear, confident, and engaging speech, effectively conveying ideas: 20%
- Critically evaluate the role of business analytics techniques in improving decision making for a data-driven organisation
- Analyse how specific business analytics techniques influence critical success factors
2 Project (applied)
Assessment 2 requires students to adhere to the guidelines on the use of artificial intelligence tools as specified in the Artificial Intelligence Assessment Scale (AIAS). Any misuse or lack of disclosure regarding the use of AI tools will be considered a breach of academic integrity.
Assessment 2 must be completed at AIAS Level 1. You must not use AI at any point to complete the assessment task.
Assessment 2 is designed for students to apply fundamental data analytics tools and/or techniques. The assessment involves writing a 2000-words business report responding to assessment questions related to specific cases and the numerical data files that store information specific to the cases will be provided on the unit website. Submit your business report including excel spreadsheet and/or any relevant calculations, with a cover sheet showing the unit's name and number, assessment number, your name and student number. You can discuss your assessment ideas in the unit Discussion Forum (Case Study), before you complete and submit the assessment.
Week 7 Friday (1 May 2026) 11:45 pm AEST
Week 7 Friday (1 May 2026)
Grades and feedback comments are released in Moodle. Feedback Studio and the Grade book are the designated platforms for reviewing outcomes from the assessment process
Your report analysis, recommendations and presentation will be assessed according to the following criteria.
- Excel Spreadsheet: Demonstrated understanding of data preparation and analytics with techniques and/or tools that are related to the questions posed: 10%
- Excel Spreadsheet: Accurately suggest and develop the model for detailed analysis in relation to the case studies: 20%
- *Demonstrated a clear understanding of the question posed by conducting a thorough literature review: 15%
- Able to articulate and evaluate case application to provide managerial insights and practical limitations based on quantitative outcomes: 20%
- Provide appropriate and well-structured, concise and clear expression of evidence-based arguments in statistical analysis: 15%
- Provide a clear flow of thought throughout the business report, evidenced by succinct Executive Summary, Introduction, and Conclusion: 10%
- Adherence to APA Reference format and In-Text Citations: 5%
- Clarity of written expression, grammar, spelling: 5%
*Insights from a minimum of 10 academic journal articles must be incorporated in the analysis:
Report length 2000-words. However, the summary, table of contents, reference list and appendices are excluded from a report’s word count.
Submissions must include an Excel spreadsheet file by showing clear Excel formulas used and calculations, data visualisations, and data analytics.
Submissions must be in Professional Report format using Word with 1.5 line spacing and Times Roman 12-point font.
Late submissions will also be penalised at the rate of "five percent of the total marks available for the assessment each calendar day (full or part) it is overdue" (Policy: Assessment of Coursework section 3.2.4)
- Critically evaluate the role of business analytics techniques in improving decision making for a data-driven organisation
- Apply descriptive business analytics techniques to assist managers to solve business problems
- Apply predictive and prescriptive business analytics techniques to assist managers to solve business problems.
3 Project (applied)
Assessment 3 requires students to adhere to the guidelines on the use of artificial intelligence tools as specified in the Artificial Intelligence Assessment Scale (AIAS). Any misuse or lack of disclosure regarding the use of AI tools will be considered a breach of academic integrity.
Assessment 3 must be completed at AIAS Level 1. You must not use AI at any point to complete the assessment task.
Assessment 3 is designed for students to apply analytical techniques and/or methods for solving a real-world application in your chosen area. The assessment involves writing a 2500-word business report responding to assessment questions related to specific topics and/or decision-making analysis. Submit your 2500-word professional report including excel spreadsheet and/or any relevant calculations through Turnitin, Moodle, with a cover sheet showing unit name and number, assessment number, your name and student number. Assessment details and guideline will be provided on the unit website. You can discuss your assessment ideas in the unit Discussion Forum (Applied Project), before you complete and submit the assessment.
Week 12 Tuesday (2 June 2026) 11:45 pm AEST
Return Date to Students Results and feedback will be made available on the unit website after Grade Certification.
Your report analysis, recommendations and presentation will be assessed according to the following criteria.
- Excel Spreadsheet: Demonstrated understanding of data preparation and analytics that are related to the questions posed: 10%
- Excel Spreadsheet: Accurately suggest and develop the model for detailed analysis in relation to the applications: 20%
- *Critical evaluation and integration of relevant academic and literature to provide theoretical and practical aspects: 15%
- Able to articulate and evaluate scenario modelling to provide managerial insights and practical limitations based on quantitative outcomes: 15%
- Provide appropriate and well-structured, concise and clear expression of decision-making arguments in terms of theoretical and practical elements 20%
- Provide a clear flow of thought throughout the business report, evidenced by succinct Executive Summary, Introduction, and Conclusion: 10%
- Adherence to APA Reference format and In-Text Citations: 5%
- Clarity of written expression, grammar, spelling: 5%
*Insights from a minimum of 10 academic journal articles must be incorporated in the analysis:
Report length 2500-words. However, the summary, table of contents, reference list and appendices are excluded from a report’s word count.
Submissions must include an Excel spreadsheet file by showing clear Excel formulas used and calculations, data visualisations, and data analytics Submissions must be in Business Report format using Word with 1.5 line spacing and Times Roman 12-point font.
Late submissions will also be penalised at the rate of "five percent of the total marks available for the assessment each calendar day (full or part) it is overdue" (Policy: Assessment of Coursework section 3.2.4)
- Analyse how specific business analytics techniques influence critical success factors
- Apply descriptive business analytics techniques to assist managers to solve business problems
- Apply predictive and prescriptive business analytics techniques to assist managers to solve business problems.
As a CQUniversity student you are expected to act honestly in all aspects of your academic work.
Any assessable work undertaken or submitted for review or assessment must be your own work. Assessable work is any type of work you do to meet the assessment requirements in the unit, including draft work submitted for review and feedback and final work to be assessed.
When you use the ideas, words or data of others in your assessment, you must thoroughly and clearly acknowledge the source of this information by using the correct referencing style for your unit. Using others’ work without proper acknowledgement may be considered a form of intellectual dishonesty.
Participating honestly, respectfully, responsibly, and fairly in your university study ensures the CQUniversity qualification you earn will be valued as a true indication of your individual academic achievement and will continue to receive the respect and recognition it deserves.
As a student, you are responsible for reading and following CQUniversity’s policies, including the Student Academic Integrity Policy and Procedure. This policy sets out CQUniversity’s expectations of you to act with integrity, examples of academic integrity breaches to avoid, the processes used to address alleged breaches of academic integrity, and potential penalties.
What is a breach of academic integrity?
A breach of academic integrity includes but is not limited to plagiarism, self-plagiarism, collusion, cheating, contract cheating, and academic misconduct. The Student Academic Integrity Policy and Procedure defines what these terms mean and gives examples.
Why is academic integrity important?
A breach of academic integrity may result in one or more penalties, including suspension or even expulsion from the University. It can also have negative implications for student visas and future enrolment at CQUniversity or elsewhere. Students who engage in contract cheating also risk being blackmailed by contract cheating services.
Where can I get assistance?
For academic advice and guidance, the Academic Learning Centre (ALC) can support you in becoming confident in completing assessments with integrity and of high standard.
What can you do to act with integrity?
