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MAF759 - Financial Data Analytics

Year:	2024 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:	This unit is only available to students enrolled in M530, M535, M630, M720, M750, M755, M794
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. This will include educator guided online learning activities within the unit site.
Educator-facilitated (scheduled) learning activities - on-campus unit enrolment:	1 x 3 hour on-campus seminar (recordings provided) each week
Educator-facilitated (scheduled) learning activities - online unit enrolment:	1 x 2 hour recorded lecture and 1 x 1 hour online seminar (recordings provided) each week

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 knowledge and capabilities GLO4: Critical thinking
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 GLO4: Critical thinking

Assessment

Assessment Description	Student output	Grading and weighting (% total mark for unit)	Indicative due week
Assessment 1: (Individual) (Online) Multiple Choice Quiz	20 minutes	10%	Weeks 4 & 5
Assessment 2: (Individual) Problem Based Written Assignment (Quantitative)	2000 words	40%	Week 10
End-of-unit assessment task: Written	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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