AFIN8015 – Financial Data Science

2024 – Session 1, In person-scheduled-intensive, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Guy Schofield
Credit points Credit points
Prerequisites Prerequisites
(Admission to MActPrac) or (Admission to MBkgFin or MCom and (AFIN8018 or ACST6003)) or ACCG6003
Corequisites Corequisites
Co-badged status Co-badged status
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.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Critically examine core predictive and classification methods in financial data science.
  • ULO2: Evaluate and apply data analytics skills using computer software tools to solve real-world problems in the finance industry.
  • ULO3: Apply working knowledge of advanced methods in financial data science to extract and report insights from financial data in various forms.

General Assessment Information

Late submissions of assessments

Unless a Special Consideration request has been submitted and approved, no extensions will be granted. There will be a deduction of 10% of the total available assessment-task marks made from the total awarded mark for each 24-hour period or part thereof that the submission is late. Late submissions will only be accepted up to 96 hours after the due date and time.

No late submissions will be accepted for timed assessments – e.g., quizzes, online tests.

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.

Assessment Tasks

Name Weighting Hurdle Due
Financial Data Analysis 1 45% No Week 6
Financial Data Analysis 2 55% No Week 11

Financial Data Analysis 1

Assessment Type 1: Project
Indicative Time on Task 2: 30 hours
Due: Week 6
Weighting: 45%

Students will be required to analyse real world financial data sets using relevant descriptive statistics and visualisation techniques.

On successful completion you will be able to:
  • Critically examine core predictive and classification methods in financial data science.
  • Evaluate and apply data analytics skills using computer software tools to solve real-world problems in the finance industry.

Financial Data Analysis 2

Assessment Type 1: Project
Indicative Time on Task 2: 40 hours
Due: Week 11
Weighting: 55%

Students will conduct quantitative and qualitative analysis using data science tools and techniques and present the findings.

On successful completion you will be able to:
  • Critically examine core predictive and classification methods in financial data science.
  • Apply working knowledge of advanced methods in financial data science to extract and report insights from financial data in various forms.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

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:

  • Lantz, B. (2019). Machine Learning with R: Expert Techniques for Predictive Modeling, 3rd Edition (3rd ed.. ed.): Birmingham: Packt Publishing, Limited.
  • Boehmke, P. D. B. C. (2016). Data Wrangling with R. Cham: Cham: Springer International Publishing.
  • Pathak, M. A. (2014). Beginning data science with R: Springer.
  • Nolan, D., & Lang, D. T. (2015). Data Science in R (1 ed.).
  • John, M., & Nina, Z. (2014). Practical Data Science with R, Second Edition: Manning Publications.
  • Chinnamgari, S. (2019). R Machine Learning Projects (1 ed.): Packt Publishing.
  • Mathur, P. (2019). Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. Berkeley, CA: Berkeley, CA: Apress.
  • Nataraj, D., Ricardo Anjoleto, F., & Vitor Bianchi, L. (2018). Hands-On Data Science with R: Packt Publishing.
  • Dayal, V. (2020). Quantitative Economics with R : A Data Science Approach / by Vikram Dayal (1st ed. 2020. ed.): Singapore : Springer Singapore : Imprint: Springer.
  • Simon, W. (2016). Big Data Analytics with R: Packt Publishing.
  • Mailund, T. (2017). Beginning Data Science in R: Data Analysis, Visualization, and Modelling for the Data Scientist. Berkeley, CA: Berkeley, CA: Apress.
  • Choe, G., & Springer International Publishing Ag. (2016). Stochastic analysis for finance with simulations (Universitext).
  • Singh, A., & Allen, David E. (2017). R in finance and economics : A beginner's guide / Abhay Kumar Singh, David Edmund Allen.

Unit Schedule

Please refer to iLearn.

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Unit information based on version 2024.03 of the Handbook