Students

AFIN2070 – Financial Data Analytics

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor and Lecturer
Hieu Nguyen
h.nguyen@mq.edu.au
Contact via Email
Room 536, 4ER Building, 4 Eastern Road
Thursday, 2:15 PM-3:15 PM (by appointment)
Credit points Credit points
10
Prerequisites Prerequisites
AFIN1002 and (STAT1250 or STAT1170 or STAT1371)
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit introduces students to the fundamental process of data analytics in finance. It focuses on developing critical computational, statistical, and other contemporary analytical skills that are essential for people conducting data-driven financial analytics. With an emphasis on applied learning informed by a strong theoretical foundation, the lectures use real-data examples to discuss contemporary topics such as data management and visualisation, financial risk analysis and prediction, regression and classification methods, and clustering. Students will practice their learned concepts and analytical skills through applied data-driven case studies using computer software tools and industry-standard financial databases.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

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

  • ULO1: Critically examine core predictive and classification methods in financial data analytics
  • 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 data analytics to extract and report insights from financial data in various forms.

General Assessment Information

Late Submission Penalties

If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days. Submissions more than 7 days late will receive a mark of 0.

Example 1 (out of 100):

If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.

Example 2 (out of 30):

If you score 27/30 but submit 20 hours late, you will lose 1.5 marks and receive 25.5/30.

Extensions

Automatic short extension: Some assessments are eligible for automatic short extension. You can only apply for an automatic short extension before the due date.

Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration. Need help? Review the Special Consideration page for further details.

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI Approach
Professional practice: Industry database training 30% No 23/04/2026 Individual Yes Open AI
Professional Practice: Applied data analysis 30% No 21/05/2026 Individual Yes Open AI
Formal examination 40% No Exam Period Individual No Observed

Professional practice: Industry database training

Assessment Type 1: Professional task
Indicative Time on Task 2: 20 hours
Due: 23/04/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open AI

The purpose of this assessment is to enable you to develop expertise in utilizing and applying quantitative analysis techniques to industry databases.

You will complete a training module provided by a financial database provider. In addition, you will extract data from the financial database and perform data analysis tasks to solve financial problems.

Skills in focus:

  • Critical thinking and problem solving
  • Digital skills
  • Work readiness
  • Discipline knowledge

Deliverable(s): Complete financial database certification and submit a written report [max 2,500 words]

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

Professional Practice: Applied data analysis

Assessment Type 1: Professional task
Indicative Time on Task 2: 15 hours
Due: 21/05/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: Yes
AI Approach: Open AI

The purpose of this assessment is for you to practice in-depth finanical data analysis.

You will conduct financial data analytics tasks and provide insights based on the information obtained.

Skills in focus:

  • Critical thinking and problem solving
  • Communication skills
  • Digital skills
  • Work readiness
  • Discipline knowledge

Deliverable(s): Python code and written report submission [max 2,500 words]

Individual assessment
On successful completion you will be able to:
  • Critically examine core predictive and classification methods in financial data analytics
  • Evaluate and apply data analytics skills using computer software tools to solve real-world problems in the finance industry.
  • Apply working knowledge of data analytics to extract and report insights from financial data in various forms.

Formal examination

Assessment Type 1: Examination
Indicative Time on Task 2: 35 hours
Due: Exam Period
Weighting: 40%
Groupwork/Individual: Individual
Short extension 3: No
AI Approach: Observed

The purpose of this assessment is for you to demonstrate the expertise you have gained in this unit.   You will participate in a 2-hour exam held during the University Examination period. Important information about the exam will be made available on the unit iLearn page. You should also review the MQ Exams website for general tips.   Deliverable(s): Formal exam Individual assessment
On successful completion you will be able to:
  • Critically examine core predictive and classification methods in financial data analytics
  • Evaluate and apply data analytics skills using computer software tools to solve real-world problems in the finance industry.
  • Apply working knowledge of data analytics 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
  • Academic Success 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.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

Delivery and Resources

Please find the details of delivery and resources in iLearn.

Unit Schedule

Lectures: Thursday 12:30 Week 1-13

Tutorials: Week 2-13 

Week Topic
1 Introduction: financial data analytics and Python
2 Financial data types, Datasets in Python
3 Data cleaning and manipulation
4 Visualize and summarize data
5 Statistical distributions and Value at Risk
6 Relationship between variables & portfolio optimization 
7 Regression analysis
8 Regression analysis 2
9 Time series analysis
10 Machine learning: regression algorithms
11 Machine learning: classification algorithms
12 Machine learning: Model evaluation
13 Revision

This schedule is subject to minor revisions when needed

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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.

Student Code of Conduct

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

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 connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

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.

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Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

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The Library provides online and face to face support to help you find and use relevant information resources. 

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Student Enquiries

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IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

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Unit information based on version 2026.03 of the Handbook