Unit convenor and teaching staff |
Unit convenor and teaching staff
Abhay Singh
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
Admission to MRes
|
Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
AFIN8015
|
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:
Late Assessment Submission Penalty (written assessments)
Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11.55pm. A 1-hour grace period is provided to students who experience a technical concern.
For any late submissions of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Online Quiz | 5% | No | Week 3 |
Financial Data Analysis 1 | 40% | No | Week 6 |
Financial Data Analysis 2 | 55% | No | Week 11 |
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: 40%
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 11
Weighting: 55%
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 |
Technology Used and Required: |
Necessary technology: Computer with R and RStudio software, Excel, 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: |
Teaching and Learning Activities The teaching in the unit will be interactive case study style delivery where financial data science classification and predictive 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:
|
Check the unit's ilearn page
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct
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
At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
The Library provides online and face to face support to help you find and use relevant information resources.
Macquarie University offers a range of Student Support Services including:
Got a question? Ask us via AskMQ, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Unit information based on version 2023.03 of the Handbook