MAF759 - Financial Data Analytics
Unit details
| Year: | 2023 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Ruipeng Liu |
| Cohort rule: | Must be enrolled in courses M530, M535, M630, M720, M740, M750, M755, M770, M794 or D712 |
| Prerequisite: | Nil |
| Corequisite: | Nil |
| Incompatible with: | MAF904 |
| Typical study commitment: | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. |
| Scheduled learning activities - campus: | 1 x 3 hour seminar per week |
| Scheduled learning activities - online: | Online independent and collaborative learning activities including 1 x 1 hour weekly scheduled online seminar |
Content
This unit will enable students to understand the fundamental data analytical methods and tools used in finance. It introduces financial mathematical concepts, statistics and econometrics that are essential tools for financial data analytics. In addition, the unit covers an introduction of machine learning and big data as well as numerical techniques and data visualisation skills using Excel and Tableau to facilitate practical business decision making.
| ULO | These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: | Deakin Graduate Learning Outcomes |
|---|---|---|
| ULO1 | Identify critical elements in statistical analysis and analyse financial data using non-parametric hypothesis testing and parametric regression analysis. | GLO1: Discipline-specific |
| ULO2 | Apply commonly used quantitative methods, appropriate digital technologies including statistical software to collect and analyse financial and nonfinancial data to provide solutions to various financial problems. | GLO3: Digital literacy GLO5: Problem Solving |
| ULO3 | Distinguish between supervised and unsupervised machine learning and evaluate the fit of a machine learning algorithm in a big data project. | GLO3: Problem solving |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Assessment 1: (Individual) Multiple Choice Quiz (Online) | 20 minutes | 10% | Weeks 4 & 5 |
| Assessment 2: (Individual) Problem Based Written Assignment (Quantitative) | 2000 words | 40% | Week 9 |
| End-of-unit assessment task | 2 hours | 50% | End-of-unit assessment period |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Hurdle requirement
Hurdle requirement: achieve at least 50% of the marks available on the end-of-unit assessment to evidence a minimum proficiency in the aligned finance discipline learning outcomes included in this unit.
Learning Resource
The texts and reading list for the unit can be found on the University Library via the link below: MAF759 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
Unit Fee Information
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