Course Coordinator:Puneet Vatsa (pvatsa@usc.edu.au) School:School of Business and Creative Industries
UniSC Sunshine CoastUniSC Adelaide |
Blended learning | Most of your course is on campus but you may be able to do some components of this course online. |
Online |
Online | You can do this course without coming onto campus, unless your program has specified a mandatory onsite requirement. |
Please go to unisc.edu.au for up to date information on the
teaching sessions and campuses where this course is usually offered.
In this course, you will develop advanced financial analysis skills using contemporary business intelligence and analytics tools. You will apply predictive analytics and time-series analysis to model financial data, visualize results, and effectively communicate insights to diverse stakeholders.
| Activity | Hours | Beginning Week | Frequency |
| Blended learning | |||
| Learning materials – Interactive online learning activities. | 2hrs | Week 1 | 6 times |
| Tutorial/Workshop 1 – Scheduled face to face workshops. | 3hrs | Week 1 | 6 times |
| Information session – Additional Information Sessions | 1hr | Week 1 | 2 times |
| Online | |||
| Learning materials – Interactive online learning activities. | 2hrs | Week 1 | 6 times |
| Tutorial/Workshop 1 – Scheduled, live, online workshops. | 3hrs | Week 1 | 6 times |
| Information session – Additional Information Sessions | 1hr | Week 1 | 2 times |
700 Level (Specialised)
12 units
| Course Learning Outcomes On successful completion of this course, you should be able to... | Graduate Qualities Mapping Completing these tasks successfully will contribute to you becoming... | Professional Standard Mapping * Association to Advance Collegiate Schools of Business | |
| 1 | Explain key concepts and theoretical foundations of financial analytics and their role in strategic decision-making. | Knowledgeable |
PC6.2
|
| 2 | Apply quantitative and time-series techniques to model risk, return, and financial performance in real-world contexts. | Creative and critical thinker |
PC1.3, PC6, PC6.1, PC6.2 |
| 3 | Use business analytics tools to develop, evaluate, and communicate predictive insights that support financial problem-solving. | Empowered |
PC3, PC3.1 |
| 4 | Create and communicate financial insights using effective visual, written, and oral formats suited to both technical and non-technical stakeholders. | Empowered |
PC1.3
|
| CODE | COMPETENCY |
| Association to Advance Collegiate Schools of Business | |
| PC1.3 | Digital Literacy |
| PC3 | Creative and Critical Thinking |
| PC3.1 | Problem Solving |
| PC6 | Career-adaptive |
| PC6.1 | Adaptability |
| PC6.2 | Discipline Knowledge |
Refer to the UniSC Glossary of terms for definitions of “pre-requisites, co-requisites and anti-requisites”.
Enrolled in any PGRD program.
Not applicable
Not applicable
Not applicable
Not applicable
Standard Grading (GRD)
| High Distinction (HD), Distinction (DN), Credit (CR), Pass (PS), Fail (FL). |
Formative feedback will be provided using various methods in the first few weeks of the session.
| Delivery mode | Task No. | Assessment Product | Individual or Group | Weighting % | What is the duration / length? | When should I submit? | Where should I submit it? |
| All | 1 | Oral and Written Piece | Individual | 50% | Students will be asked to submit their R script and a 250-word reflection. They will explain their code, thinking, and findings, and communicate key insights in a recorded presentation lasting 6-10 minutes. |
Week 4 | Online Submission |
| All | 2 | Oral and Written Piece | Individual | 50% | Students will be asked to submit their R script and a 250-word reflection. They will explain their code, thinking, and findings, and communicate key insights in a recorded presentation lasting 6-10 minutes. |
Week 8 | Online Submission |
| All - Assessment Task 1:Financial Data Analysis & Insight Report | ||||||||||||||||
| Goal: | Apply programming techniques in R to analyze financial data, create effective visualizations, and communicate insights clearly to both technical and non-technical stakeholders through a recorded presentation. |
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| Product: | Oral and Written Piece | |||||||||||||||
| Authorship Statement: | ||||||||||||||||
| Format: | For this assessment, students will complete a structured financial time series analysis and submit a multi-part recorded presentation. Using R, students will analyze a financial dataset, applying appropriate techniques to explore trends, patterns, and relationships over time. First, they will record themselves presenting and explaining their R code and analytical workflow directly within RStudio. This segment should be 3–5 minutes long and must begin with the student clearly showing their ID on camera. Immediately following this, students will present their key findings and recommendations in a 2–4-minute recorded segment, using visualizations and insights drawn from their analysis to communicate with a stakeholder audience. Students must remain visible on camera throughout both segments to verify authorship. Finally, students will submit the recording, their R script, and a 250-word written reflection discussing their analytical process, challenges encountered, and how their findings could inform financial decision-making. All components are to be submitted via Canvas. |
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| Criteria: |
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| Generic Skills: | Communication, Problem solving, Information literacy |
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| All - Assessment Task 2:Predictive Modelling & Portfolio Evaluation | ||||||||||||||||
| Goal: | Demonstrate the ability to develop and evaluate predictive models using time-series techniques, analyze and model risk and return with quantitative methods, and communicate financial insights clearly to both technical and non-technical stakeholders through a recorded presentation. |
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| Product: | Oral and Written Piece | |||||||||||||||
| Authorship Statement: | ||||||||||||||||
| Format: | Students will record themselves presenting and explaining their R code and workflow directly within RStudio. This segment should be 3–5 minutes long and must begin with the student clearly showing their ID on camera. Immediately following this, students will present a PowerPoint summarizing their key findings and recommendations to a stakeholder audience. This presentation should be 2–4 minutes in length and also be recorded with the student visible throughout. At all times during both segments, students must remain on camera to verify authorship. Finally, students will submit the recording, their R script, and a 250-word written reflection discussing their analytical process, challenges encountered, and how their findings could inform decision-making. All components are to be submitted via Canvas. |
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| Criteria: |
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| Generic Skills: | Communication, Problem solving, Applying technologies, Information literacy |
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A 12-unit course will have total of 150 learning hours which will include directed study hours (including online if required), self-directed learning and completion of assessable tasks. Student workload is calculated at 12.5 learning hours per one unit.
Please note: Course information, including specific information of recommended readings, learning activities, resources, weekly readings, etc. are available on the course Canvas site– Please log in as soon as possible.
You need regular access to the resource(s) below. Many texts are available as ebooks through the Library at no additional cost.
| Required? | Author | Year | Title | Edition | Publisher |
| Recommended | Timothy R. Mayes | 2020 | Financial Analysis with Microsoft Excel | n/a | South-Western College |
Not applicable
Academic integrity is the ethical standard of university participation. It ensures that students graduate as a result of proving they are competent in their discipline. This is integral in maintaining the value of academic qualifications. Each industry has expectations and standards of the skills and knowledge within that discipline and these are reflected in assessment.
Academic integrity means that you do not engage in any activity that is considered to be academic fraud; including plagiarism, collusion or outsourcing any part of any assessment item to any other person. You are expected to be honest and ethical by completing all work yourself and indicating in your work which ideas and information were developed by you and which were taken from others. You cannot provide your assessment work to others. You are also expected to provide evidence of wide and critical reading, usually by using appropriate academic references.
In order to minimise incidents of academic fraud, this course may require that some of its assessment tasks, when submitted to Canvas, are electronically checked through Turnitin. This software allows for text comparisons to be made between your submitted assessment item and all other work to which Turnitin has access.
Eligibility for Supplementary Assessment
Your eligibility for supplementary assessment in a course is dependent of the following conditions applying:
(a) The final mark is in the percentage range 47% to 49.4%; and
(b) The course is graded using the Standard Grading scale
Late submissions may be penalised up to and including the following maximum percentage of the assessment task’s identified value, with weekdays and weekends included in the calculation of days late:
(a) One day: deduct 5%;
(b) Two days: deduct 10%;
(c) Three days: deduct 20%;
(d) Four days: deduct 40%;
(e) Five days: deduct 60%;
(f) Six days: deduct 80%;
(g) Seven days: A result of zero is awarded for the assessment task.
The following penalties will apply for a late submission for an online examination:
Less than 15 minutes: No penalty
From 15 minutes to 30 minutes: 20% penalty
More than 30 minutes: 100% penalty
For more information on Academic Learning & Teaching categories including:
For more information, visit https://www.usc.edu.au/explore/policies-and-procedures#academic-learning-and-teaching
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