220789 Financial Analytics

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2025 is available in the Archives.

UTS: Business: Accounting
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

Requisite(s): (230708 Foundation Studio AND 240753 Customer Analytics AND 320146 Data Visualisation and Visual Analytics AND (430031 Python Programming for Data Processing OR 420047 Data Processing Using Python)) OR (220800 Accounting for Decision Makers AND 220700 Data Driven Decision Making)
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Anti-requisite(s): 22789 Financial Analytics

Description

Financial Analytics focuses on the application of data analytics techniques to derive valuable insights and drive decision-making utilising financial data. This subject equips students with the skills and knowledge necessary to analyse financial and non-financial data, uncovering patterns, trends, and relationships that can inform strategic business decisions. Throughout the subject, students explore the IMPACT analytics framework as a structured approach to conducting financial analytics projects. They learn to identify relevant business questions, preprocess, and analyse large datasets, apply appropriate analytical techniques, and effectively communicate insights to stakeholders. They gain hands-on experience with data analytics tools and techniques, including data visualisation and statistical analysis to tackle real-world financial analytics problems.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1.	Apply data analysis methods using spreadsheets and other tools
2.	Evaluate the role and impact of business analytics for accounting, reporting and decision making
3.	Apply appropriate quantitative analytical techniques to organisational decision making using appropriate technology
4.	Effectively interpret results and assumptions of data analysis and analytical modelling and communicate them – verbally and in written form – to relevant stakeholders

Contribution to the development of graduate attributes

This subject builds on the foundation provided in the other, foundational, subjects of the Master of Business Analytics. It builds on the analytical knowledge and skills gained in those study components and contributes to the objectives of the degree by extending their analytics knowledge into the context of accounting and finance.

This subject is aligned with the following Graduate Attribute(s):

Intellectual rigour and innovative problem solving
Professional and technical competence

Teaching and learning strategies

Orientation activities
Preparation for the Session - students are expected to undertake activities prior to the first week. These activities (approximately two hours in duration) include online readings, videos (database searching) and interaction with peers and are important in helping students prepare for the subject’s Assessment Tasks. This also provides students with an opportunity to meet and interact with peers.
Students will learn through independent learning activities, group work, peer review, and participation in collaborative online sessions through the learning management system.

Independent learning activities
Relevant readings, videos and activities will be made available online relevant to the topic of the week. Students are expected to come to the collaborative online sessions prepared. This will enhance the students’ ability to progress successfully throughout the subject and complete assessment items effectively. The online material aims to enhance students’ understanding of the topic or delve deeper into a more specific area. Information and links to all these learning activities can be accessed via Canvas as well as the subject outline.

Online collaborative sessions
Online collaborative sessions will provide opportunities for group activities and discussion, self-assessment, peer review and formative feedback from the subject facilitator. Online collaborative sessions will be conducted at a time that enables the majority of students to contribute.

Feedback
Feedback will be frequent and takes several forms including self-assessment and peer review. Formative feedback throughout the subject aims to increase student performance at summative assessments.

Content (topics)

Introduction to Financial Analytics
Spreadsheet modelling, data visualisation and exploring financial data
Understanding and preparing financial data
Statistical inference and hypothesis testing
Regression analysis
Time series analysis and forecasting
Financial forecasting and portfolio analysis
Optimisation and Simulation

Assessment

Assessment task 1: Progress Quiz (Individual)*

Objective(s):	This addresses subject learning objective(s): 1 and 2
Weight:	10%
Length:	30 minutes
Criteria:	The quiz will be graded based on the correctness and accuracy of students answers to numerical and conceptual questions. *Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Assessment task 2: Video Presentation (Individual)*

Weight:	40%
Length:	5 – 8 minutes
Criteria:	Demonstration of a clear understanding and ability to apply concepts and techniques covered in the subject. *Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Weight:

40%

Length:

5 – 8 minutes

Criteria:

Demonstration of a clear understanding and ability to apply concepts and techniques covered in the subject.

*Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Assessment task 3: Case Study (Individual)

Objective(s):	This addresses subject learning objective(s): 1, 2, 3 and 4
Weight:	50%
Length:	1,000 – 1,200 words
Criteria:	Students will be expected to show a holistic understanding of materials taught in the subject through a demonstrated ability to apply those in a real-world analogue case study.

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

Required texts

There is no required textbook. Resources will be made available through the learning management system as required.

References

Camm, Cochran, Fry, Ohlmann, Anderson, Sweeney and Williams (2019), Business Analytics – Descriptive Predictive Prescriptive, 3rd edition, Cengage: Boston.

Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000), CRISP-DM 1.0 Step-by-step data mining guide

Minelli, M., Chambers, M. and Dhiraj, A. (2013), Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses, Wiley.