Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Business School
Business Analytics involves the application of statistics to understand and solve business problems. This course provides the statistical foundation for the Master of Business Analytics program, by introducing students to relevant statistical concepts required for data analysis. In addition, through a practical component, the course introduces students to applying appropriate statistical analysis through R.
This course will give you a foundational understanding of business data and the role of statistical analysis in creating value. This is a technical course in which we will empower you in analytics by introducing you to statistical computation using the R language, as well as relevant theory to help guide your understanding. Throughout the course, you will apply what you learn to a series of data sets from a range of business contexts. The assessments will be based around communicating your insights to a technical manager and other stakeholders.
The course is divided into four modules:
- Exploring Data
- Evidence from Data
- Model Building
- Model Evaluation
Each topic within a module begins by leading you through a business example, showing how statistical analysis is used to describe and model the data. You then put this into practice by conducting the analysis yourself using R. We have an Ed discussion board where you can share your findings and ask questions. There is then a formative quiz, for you to check your understanding. Each week we then get together for a live session, an important opportunity to reflect on the content and look at other examples together. Recordings of these sessions will be posted on Blackboard afterwards.
Check in to Blackboard regularly to see announcements. Blackboard also has all the details for completion and submission of your assessments.
Course requirements
Incompatible
You can't enrol in this course if you've already completed the following:
DATA7202 and ECON7300
Restrictions
Restricted to students in the MBusAn program
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
There will be a one hour online (via Zoom), live session at 4:00 pm (Brisbane time) Wednesday weeks 1-2, 4-8, and 10-12. See Blackboard for details. There will be no activities on the Ekka Show day public holiday. Alternative arrangements will be made and communicated via Blackboard, if required.
Aims and outcomes
The aim of this course is to provide foundational skills in the application of statistical methodology to understand and solve business problems.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Perform exploratory data analysis to describe data.
LO2.
Construct and evaluate models for inferential statistical analysis for real-world problem solving.
LO3.
Formulate data-driven insights and value propositions for stakeholders.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation, Project | Exploratory Data Analysis | 30% |
16/08/2024 3:00 pm |
| Tutorial/ Problem Set |
Analytics Practice
|
20% |
4/10/2024 3:00 pm |
| Paper/ Report/ Annotation, Presentation | Technical Report and Stakeholder Presentation | 50% |
25/10/2024 3:00 pm |
Assessment details
Exploratory Data Analysis
- Mode
- Written
- Category
- Paper/ Report/ Annotation, Project
- Weight
- 30%
- Due date
16/08/2024 3:00 pm
- Learning outcomes
- L01
Task description
The objective of this assignment is for you to undertake an exploratory data analysis to summarise and highlight key aspects and special features of business data. You will consolidate your knowledge and develop skills in data analysis to solve business problems, provide insights and enhance your presentation and communication skills in a business context. This will further strengthen your ability to tackle problems in business data analysis and visualisation.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Submit via Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission of assessment/Extensions to assessment due date
A student may apply for an extension to assessment due date if they are unable to meet an assessment deadline due to extenuating circumstances.
Please refer to my.UQ for information on applying for assessment extension: Applying for an assessment extension
Additional information on extensions can be found within the Assessment Procedure.
Students applying for an extension must submit a request through my.UQ and provide evidence of their circumstances, as soon as it becomes evident that an extension is needed, but no later than the assessment item submission due date and time.
Requests for extensions received after the assessment item submission due date and time must include evidence of the reasons for the late request and will require the decision maker listed in the Student Grievance Resolution Procedures to accept the request for consideration.
In the Business School, all extension requests are processed by the Business School Exams Team.
Unless there is pedagogical reason provided in the ECP regarding limitations on extensions, Business School extensions will generally be limited to one week in the first instance.
In exceptional circumstances, approved extensions may be granted for more than one week but will not exceed four weeks in total. Where a student is incapacitated for a period exceeding four weeks of the teaching period, they might be advised to apply for removal of course.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Please refer to the following link for more details regarding the provisioning of Late Submission penalties - Click Here
Analytics Practice
- Online
- Mode
- Written
- Category
- Tutorial/ Problem Set
- Weight
- 20%
- Due date
4/10/2024 3:00 pm
- Learning outcomes
- L01, L02
Task description
The modules contain a total of eight Analytics Practice tasks. Each task is an important opportunity to put the technical skills you are learning into practice by working with a new data set and using RStudio to answer five numerical questions. Correctly answering each question is worth 0.5 marks towards your overall grade (for a maximum of 20 marks). You can attempt each question as many times as you like before the due date. (There is no penalty for incorrect answers.)
You are encouraged to discuss how to answer the questions with your peers on Ed Discussion.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Complete via Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission of assessment/Extensions to assessment due date
A student may apply for an extension to assessment due date if they are unable to meet an assessment deadline due to extenuating circumstances.
Please refer to my.UQ for information on applying for assessment extension: Applying for an assessment extension
Additional information on extensions can be found within the Assessment Procedure.
Students applying for an extension must submit a request through my.UQ and provide evidence of their circumstances, as soon as it becomes evident that an extension is needed, but no later than the assessment item submission due date and time.
Requests for extensions received after the assessment item submission due date and time must include evidence of the reasons for the late request and will require the decision maker listed in the Student Grievance Resolution Procedures to accept the request for consideration.
In the Business School, all extension requests are processed by the Business School Exams Team.
Unless there is pedagogical reason provided in the ECP regarding limitations on extensions, Business School extensions will generally be limited to one week in the first instance.
In exceptional circumstances, approved extensions may be granted for more than one week but will not exceed four weeks in total. Where a student is incapacitated for a period exceeding four weeks of the teaching period, they might be advised to apply for removal of course.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Please refer to the following link for more details regarding the provisioning of Late Submission penalties - Click Here
Technical Report and Stakeholder Presentation
- Mode
- Oral, Written
- Category
- Paper/ Report/ Annotation, Presentation
- Weight
- 50%
- Due date
25/10/2024 3:00 pm
- Learning outcomes
- L02, L03
Task description
The objective of this assessment is for you to work with a complex collection of data files in order to create a value proposition for a stakeholder. You will use techniques of data wrangling in R to generate and explore suitable variables to address a business question. You will then select and justify the relevant theory and advanced tools that can be applied to the variables. Your analysis should demonstrate the application the three core methods from Modules 3 and 4:
- Multiple linear regression
- Logistic regression and multiple logistic regression
- Time series
You will then present your insights from this analysis as a technical report to your supervisor and as a persuasive presentation to the stakeholders.
Please Note: The presentation will be recorded for marking purposes.
This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Further details of submission will be provided on Blackboard
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Late submission of assessment/Extensions to assessment due date
A student may apply for an extension to assessment due date if they are unable to meet an assessment deadline due to extenuating circumstances.
Please refer to my.UQ for information on applying for assessment extension: Applying for an assessment extension
Additional information on extensions can be found within the Assessment Procedure.
Students applying for an extension must submit a request through my.UQ and provide evidence of their circumstances, as soon as it becomes evident that an extension is needed, but no later than the assessment item submission due date and time.
Requests for extensions received after the assessment item submission due date and time must include evidence of the reasons for the late request and will require the decision maker listed in the Student Grievance Resolution Procedures to accept the request for consideration.
In the Business School, all extension requests are processed by the Business School Exams Team.
Unless there is pedagogical reason provided in the ECP regarding limitations on extensions, Business School extensions will generally be limited to one week in the first instance.
In exceptional circumstances, approved extensions may be granted for more than one week but will not exceed four weeks in total. Where a student is incapacitated for a period exceeding four weeks of the teaching period, they might be advised to apply for removal of course.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Please refer to the following link for more details regarding the provisioning of Late Submission penalties - Click Here
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 29 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30 - 46 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
Grades will be allocated according to University-wide standards of criterion-based assessment.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Supplementary Assessment
Should you fail a course with a grade of 3, or a non-graded ‘N’, you may be eligible for supplementary assessment. Refer to my.UQ for information on supplementary assessment.
Supplementary assessment may not be available for all courses, or for some of the assessment items for a course. The highest grade you can receive following supplementary assessment is a 4 or P. Details of availability of supplementary assessment for this course are set out below.
Supplementary Assessment is available for this Course.
Refer to my.UQ for how to apply for supplementary assessment.
Supplementary assessment can take any form, for example, an oral or a written exam. Students who are eligible and approved for a supplementary assessment and the form of assessment is an examination, are expected to be available to sit the supplementary exam during the University’s Deferred and Supplementary examination period. Once approved, supplementary assessment cannot be rescinded by the student.
Supplementary assessment for this course will take the form of a week long project and report.
Artificial Intelligence
The assessment tasks in this course evaluate students' abilities, skills, and knowledge without the aid of Artificial Intelligence (AI). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute misconduct under the Student Code of Conduct.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Other course materials
If we've listed something under further requirement, you'll need to provide your own.
Required
| Item | Description | Further Requirement |
|---|---|---|
| Rise Materials | Rise Modules 1-4 on Blackboard |
Recommended
| Item | Description | Further Requirement |
|---|---|---|
| Ed Discussion Board | Discussion board for the course |
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Not Timetabled |
Topic 1.1 - Univariate Analysis Self-Directed Learning: What methods can we visualise our data to gain insight into patterns of variability? How can we used statistics to summarise the patterns we see? Learning outcomes: L01 |
Seminar |
Topic 1.1 - Univariate Analysis Live session Learning outcomes: L01 |
|
Week 2 |
Not Timetabled |
Topic 1.2 - Bivariate Analysis Self-Directed Learning: How can we visualise and summarise the relationships between quantitative variables? Learning outcomes: L01 |
Seminar |
Topic 1.2 - Bivariate Analysis Live session Learning outcomes: L01 |
|
Week 3 |
Not Timetabled |
Assessment Preparation Assignment preparation |
Week 4 |
Not Timetabled |
Topic 2.1 - Hypothesis Testing Self-Directed Learning: How can we draw conclusions about population comparisons using our sample data? Learning outcomes: L02 |
Seminar |
Topic 2.1 - Hypothesis Testing There will be no activities on the Ekka Show day public holiday. Alternative arrangements will be made and communicated via Blackboard, if required. Learning outcomes: L02 |
|
Week 5 |
Not Timetabled |
Topic 2.2 - Linear Regression Self-Directed Learning: How can we determine whether a linear relationship exists between two variables? How can we use our model to make predictions? Learning outcomes: L02 |
Seminar |
Topic 2.2 - Linear Regression Live session Learning outcomes: L02 |
|
Week 6 |
Not Timetabled |
Topic 3.1 - Multiple Regression Self-Directed Learning: How can we model the relationship between a response and multiple explanatory variables? How do we know which factors are important for making predictions? Learning outcomes: L02 |
Seminar |
Topic 3.1 - Multiple Regression Live session Learning outcomes: L02 |
|
Week 7 |
Not Timetabled |
Topic 3.2 - Logistic Regression Self-Directed Learning: How can we predict a categorical outcome? How can we know which factors affect the outcome? Learning outcomes: L02 |
Seminar |
Topic 3.2 - Logistic Regression Live session Learning outcomes: L02 |
|
Week 8 |
Not Timetabled |
Topic 3.3 - Time Series Self-Directed Learning: How can we understand patterns of variability over time and make forecast from past data? Learning outcomes: L02 |
Seminar |
Topic 3.3 - Time Series Live session Learning outcomes: L02 |
|
Week 9 |
Not Timetabled |
Assessment Preparation Assessment Preparation |
Mid Sem break |
No student involvement (Breaks, information) |
In-Semester Break |
Week 10 |
Not Timetabled |
Topic 4.1 - Model Selection Self-Directed Learning: What criteria can we use to evaluate our models? How can we use these criteria to select meaningful models? Learning outcomes: L03 |
Seminar |
Topic 4.1 - Model Selection Live session Learning outcomes: L03 |
|
Week 11 |
Not Timetabled |
Topic 4.2 - Regression Case Study Self-Directed Learning: What issues are important when putting theory into practice in a multiple regression model? Learning outcomes: L03 |
Seminar |
Topic 4.2 - Regression Case Study Live session Learning outcomes: L03 |
|
Week 12 |
Not Timetabled |
Topic 4.3 - Time Series Case Study Self-Directed Learning: What issues are important when putting theory into practice in time series analysis? Learning outcomes: L03 |
Seminar |
Topic 4.3 - Time Series Case Study Live session Learning outcomes: L03 |
|
Week 13 |
Not Timetabled |
Assessment Preparation Assessment Preparation |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.