Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
Abhay Singh
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
Admission to Master of Research
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit introduces the fundamental process of data science for finance to students with an interest in the rapidly growing area of FinTech. The unit focuses on developing critical computational, statistical, and other contemporary analytical skills that are essential for people conducting the data-driven financial analytics in the FinTech area. Students will practice their learned concepts and analytical skills through applied data-driven case studies in selected data intensive domains in finance such as financial data management and visualisation, financial risk analysis and prediction, consumer analytics, trading etc. Financial Data Science is a course with an emphasis on applied learning informed by strong theoretical foundation. The lectures combine discussion on contemporary methods in data science such as Regression and Classification methods, Data Management and Visualisation methods, clustering, Machine Learning methods etc., with worked examples using real data. Students will use computer terminals with access to Excel and programming tools such as SQL, R, Python etc, and industry standard financial databases. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Assessment criteria for all assessment tasks will be provided on the unit iLearn site.
It is the responsibility of students to view their marks for each within-session-assessment on iLearn within 20 days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment tasks (not including the final exam mark) will not be addressed.
Late submissions and extensions
Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
Tasks above 10% - No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission – 20% penalty). This penalty does not apply for cases in which an application for special consideration is made and approved. No submission will be accepted after solutions have been posted.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Online Quiz | 5% | No | Week 3 |
Financial Data Analysis 1 | 35% | No | Week 6 |
Financial Data Analysis 2 | 60% | No | Week 12 |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 2 hours
Due: Week 3
Weighting: 5%
The online quiz will consist of 5 to 10 multiple choice and/or short answer questions and will be available on iLearn. Please use the quiz result as an indicator of whether you are progressing satisfactorily in the unit.
Assessment Type 1: Project
Indicative Time on Task 2: 25 hours
Due: Week 6
Weighting: 35%
Students will be required to analyse real world financial data sets using relevant descriptive statistics and visualisation techniques.
Assessment Type 1: Project
Indicative Time on Task 2: 30 hours
Due: Week 12
Weighting: 60%
Students will review core predictive and classification methods in financial data science research and conduct quantitative and qualitative analysis using data science tools and techniques, and present their findings
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Required Text: |
The unit will utilise various library resources, including research papers, book chapters, case studies etc., and relevant material will be made available on ilearn. |
Unit Web Page: |
Log in via https://ilearn.mq.edu.au |
Technology Used and Required: |
Necessary technology: Computer with MS Excel, R and RStudio software, scientific or business calculator without alphanumeric capabilities, internet access. Useful technology: The unit will utilise the R software but the MATLAB and Python software environment are also very useful if you intend doing this sort of work professionally. |
Delivery Format and Other Details: |
Classes A typical class will be structured as recorded lecture(s) and a live lecture with hands on example. The two parts will mostly flow together and not separately. Please feel free to ask (and answer!) questions throughout the class. Attendance at the live sessions is expected. Teaching and Learning Activities The teaching in the unit will be interactive case study style delivery where financial modelling and forecasting methods will be discussed along with hands on examples using R. You are strongly advised to attempt all examples before the weekly lectures, and before consulting the solutions. You are encouraged to submit your workings of the class examples for further feedback. |
Recommended Readings: |
We will supplement the lecture materials with readings from journals and other textbooks. A list of relevant material will be provided on iLearn site. Following are some of the recommended readings:
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Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
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Date | Description |
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08/02/2021 | Unit is co-taught with AFIN8015 |
Unit information based on version 2021.02 of the Handbook