BFF5525 · Quantitative and data analysis in Python

Overview

This unit develops advanced foundational skills in quantitative and data analysis for financial applications using Python as the programing language. Content is organised into 2 sections: introduction to Python and quantitative investment analysis. In the former, you will be introduced to Python and its core libraries for financial applications such as NumPy and Pandas - no prior knowledge in Python or programming is required. In the latter, you will refresh your knowledge on probability and statistics and develop hands-on skills in financial data processing and visualisation, performing correlation and regression, time series analysis, simulation analysis and portfolio construction.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. gain a functional understanding of the Python programming language and its various libraries required for financial applications
2. write custom Python codes and build custom Python modules to analyse financial data and investment strategies.
3. develop a confident knowledge of fundamental concepts in probability, statistics and mathematics relevant in quantitative investment analysis
4. develop skills to efficiently process and manipulate financial databases
5. perform various financial analyses and implement quantitative models in investment strategies
6. apply critical thinking, problem solving and presentation skills to individual and/or group activities dealing with quantitative and data analysis in an individual summative assessment task the acquisition of a comprehensive understanding of the topics covered in this unit.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.