Overview
With today’s digitisation and technology development, many organisations can collect and consolidate tremendous amounts of data and store them in databases and data warehouses with ease. In business analytics, you will use a variety of computational techniques and/or methods to evaluate and analyse huge sources of data in real time for trends, patterns, classification, relationship, and other useful information. You will learn and examine data sets for statistical inference, and conduct quantitative analysis, predictive modelling, regression, data mining, and optimisation. This is a practical based core unit and will provide you with foundation knowledge to contribute to the use of various data analytics for problem solving.
Details
Pre-requisites or Co-requisites
STAT11048 Essential Statistics is an anti-requisite for this unit MGMT11169 Business Analytics. Students who completed STAT11048 Essential Statistics should not enroll in this unit MGMT11169 Business Analytics.
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 2 - 2025
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 Undergraduate 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 Student Unit and Teacher Evaluation (SUTE)
The vast quantity of online resources help students in complete weekly activities and assessments.
Continue to update online resource to include more case studies from different industry sectors.
Feedback from Student Unit and Teacher Evaluation (SUTE)
The real-world applications provided were helpful in deepening our understanding of the material.
Continue to update relevant and real-world applications.
- Analyse and reflect on key concepts of business analytics
- Apply quantitative tools and techniques to analytically identify, examine, investigate and propose solutions to business problems
- Synthesise data from a variety of sources and develop models to address practical problems in industry.
Alignment of Assessment Tasks to Learning Outcomes
| Assessment Tasks | Learning Outcomes | ||
|---|---|---|---|
| 1 | 2 | 3 | |
| 1 - Presentation - 40% | |||
| 2 - Report - 60% | |||
Alignment of Graduate Attributes to Learning Outcomes
| Graduate Attributes | Learning Outcomes | ||
|---|---|---|---|
| 1 | 2 | 3 | |
| 1 - Communication | |||
| 2 - Problem Solving | |||
| 3 - Critical Thinking | |||
| 4 - Information Literacy | |||
| 5 - Team Work | |||
| 6 - Information Technology Competence | |||
| 7 - Cross Cultural Competence | |||
| 8 - Ethical practice | |||
| 9 - Social Innovation | |||
| 10 - Aboriginal and Torres Strait Islander Cultures | |||
Textbooks
Business Analytics
5th edition (International) (2024)
Authors: Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann
Cengage Learning
Boston Boston , MA , US
ISBN: 9798214050294
Binding: Paperback
IT Resources
- CQUniversity Student Email
- Internet
- Unit Website (Moodle)
- Excel spreadsheet software
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
Module/Topic
Welcome Message and Excel Fundamentals
Chapter
Welcome to Business analysis and decision making; and Importance of using Excel Spreadsheet for Analytics
Events and Submissions/Topic
Details of Moodle site and resources available.
Expectations of student engagement with the unit.
Overview of the Assessment Items.
Module/Topic
Descriptive Statistics
Chapter
Data types and statistics; and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Data Visualisation
Chapter
Charts and data visualisation; and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Data Interpretation and Strategies
Chapter
Importance of Data Insights and Communication
Events and Submissions/Topic
Module/Topic
Modelling Uncertainty and Probability
Chapter
Probability and modelling uncertainty and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Assessment 1 Oral Assessment
Due Date: 15 Aug 2025, Friday, 11:45 pm AEST
Module/Topic
No classes will be held during this week.
Chapter
No classes will be held during this week.
Events and Submissions/Topic
Module/Topic
Continuous Probability Distributions
Chapter
Modelling real world phenomena using continuous probability distributions and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Discrete Probability Distributions
Chapter
Modelling real world phenomena using discrete probability distributions and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Statistical Inference and Sampling
Chapter
Understanding of point Estimation and sampling process and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Hypothesis Testing
Chapter
Understanding of types of errors and hypothesis testing and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Modelling of Linear Regression
Chapter
Regression modelling and relationships; and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Module/Topic
Model Reporting and Analysis
Chapter
Understanding of model analysis and Lecture notes and material are available in Moodle.
Events and Submissions/Topic
Assessment 2 Business Analytics Report
Due Date: 7 Oct 2025, Tuesday, 11:45am AEST
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
Module/Topic
Chapter
Events and Submissions/Topic
1 Presentation
The assessment evaluates students' proficiency in applying fundamental data analytics techniques using Excel spreadsheet. A case application and numerical data file are available on the unit website.
You are required to prepare an 8-minute presentation on a data analytics case application. You must 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.
Important Note:
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.
Due Date: 15 August 2025 and further information will be provided on Moodle in Week 3.
Date: 29 August 2025 Grades and feedback comments are released on the unit Moodle page.
Assessment 1 will be assessed according to the following criteria.
- Excel Spreadsheet: Demonstrates proficiency in preparing and analysing data with accuracy to support data-driven decision-making: 15%
- Excel Spreadsheet: Exhibits competence in using relevant software tools and applications: 15%
- PowerPoint Slides: Presents ideas in a clear, logical, and well-structured manner: 15%
- Demonstrates accuracy and analytical reasoning in solving quantitative problems: 20%
- Demonstrates understanding of model content with clear integration of key concepts and recommendations.: 20%
- Exhibits communication and presentation skills with clear, confident, and engaging speech: 15%
- Analyse and reflect on key concepts of business analytics
- Apply quantitative tools and techniques to analytically identify, examine, investigate and propose solutions to business problems
- Synthesise data from a variety of sources and develop models to address practical problems in industry.
2 Report
This assessment evaluates students' mastery of data analytics techniques and business problem solving skills, as applied through the utilization of Excel spreadsheet software. Students are required to complete an in-depth analysis of a business application and scenario, accompanied by a numerical data file (accessible on the unit website), and subsequently write a clear and concise 1600-word data analytics report. Submissions must include a concise data analytics report, an Excel spreadsheet and/or calculations, and a cover sheet with the following information: unit name and number, assessment number, students' names, and student identification numbers
Important Note:
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.
Due Date: 7 October 2025 and further information will be provided on Moodle in Week 7.
Assessment feedback and grades are to be released upon certification of grades (refer to assessment policy).
Assessment 2 report analysis, recommendations and presentation will be assessed according to the following criteria.
- Excel Spreadsheet: Demonstrated understanding of data preparation (e.g., missing values, imputation, removal etc.): 5%
- Excel Spreadsheet: Accurately develop model analysis, e.g. linear regression, hypotheses, correlation, statistical inference, predictive analysis etc.: 30%
- * Demonstrated a clear understanding of the question posed by conducting a thorough literature review: 15%
- Able to articulate and provide findings, insights and limitations of the model evaluation discussed: 20%
- Provide appropriate and well-structured, concise and clear expression of evaluation and analysis arguments: 10%
- Provide a clear flow of thought throughout the report, evidenced by succinct summary, intro, conclusion: 10%
- Adherence to APA reference format and in-text citation: 5%
- Clarity of written expression, grammar, spelling: 5%
Important Note:
- * At least 8 high-quality journal articles published within the last decade should be used.
- 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.
- Report length 1600-words (+/- 5%). However, the summary, table of contents, reference list and appendices are excluded from a report’s word count.
- 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 and reflect on key concepts of business analytics
- Apply quantitative tools and techniques to analytically identify, examine, investigate and propose solutions to business problems
- Synthesise data from a variety of sources and develop models to address practical problems in industry.
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?
