LEARNING & DEVELOPMENT

About

This 10-week online course consists of a structured progression of lectures and workshops, designed for programming beginners with a background in financial analysis. Students will learn the fundamental concepts and skills needed to use Python as a tool for financial modelling and analysis. They will also be prepared to continue the coding journey themselves with continued self-study. Python is the world’s most popular programming language for data & analytics, and features an enormous and active community of open-source developers and users who have contributed hundreds of thousands of useful pre-built libraries (similar to plug-in tools for Excel) for easy application to a vast body of coding and analysis problems.

Pre-requisite

None required.

Who should attend

• Financial Analysts
• Investment Bankers
• Accountants, Actuaries & Auditors
• Risk Managers
• Portfolio Managers
• Market Researchers &
• Quantative Analysts

Benefits & Outcomes

Student Experience Level

The course starts from the very basics, assuming no prior programming experience, but progresses quickly and requires a substantial time commitment to absorb the skills and materials. The volume of content is about equivalent to a full university paper. Familiarity with financial analysis concepts is assumed (e.g. discounted cash flow analysis, compound growth rates, stock performance metrics, balance sheets, profit & loss statements, etc), as is some familiarity with using MS Excel for financial modelling (this is helpful but not critical).

Connection to emerging AI technology

Python has long been a tool of choice for building and deploying Machine Learning models (Machine Learning being one branch of AI focused on numeric computation and prediction or classification). With python libraries like scikit-learn, statsmodels, or tensor-flow, you can train a random forest classifier or an ARIMA time-series forecast on a dataset and deploy your model locally in just a few lines of code.

The recent surge in capabilities and popularity of Large Language Models (LLMs) such as ChatGPT opens a new avenue for interaction between python and AI. In this case, LLMs are an outstanding tool for coding basic scripts quickly, understanding the code of others, and generally learning about python efficiently. LLMs are best used by beginners as a built-in tutor to ask questions about how to go about coding something or about what a block of code is doing. As you gain skill and understanding in python and some basic computer science concepts, LLMs become an incredible tool to leverage your new knowledge and enhance the efficiency of your programming work. The rise of interactive AI makes coding skills more powerful and useful than ever, in the same way spreadsheets enhance the usefulness of learning algebra.

Connection to “big data”

The python ecosystem is used extensively for big data applications, usually running in a cloud environment as part of a pipeline that includes other platforms and tools beyond the scope of this course. The skills and concepts we will learn are most readily applied to smaller to medium-sized data (a few million rows still counts as “medium”, so this is larger than what Excel can handle). We will be running code locally, so the limits of what you can run are determined by the memory and processing hardware in your machine. As you gain skill in using python, you can explore ways to optimise your code for running tasks on larger datasets, or to develop and deploy code in a cloud environment with scalable compute resources, but this is not covered in the course since most financial modelling does not require multi-billion row calculations. However, if your goal is to work with big data in python, this course is still an excellent place to start the journey.