INTRO TO PYTHON FOR FINANCIAL ANALYSIS
MARCH - JUNE 2025
About
This 10-week course, delivered virtually in an interactive video call setting, is 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.
Content
Course Schedule
Week |
Module |
Homework |
Activity |
| 1 | 1 | Module 1 exercises
Part 1 |
Course overview, intro to Python and examples, section assignments, meet your section of fellow students with whom you will be undertaking the small group exercises |
| 2 | 1 | Module 1 exercises
Part 2 |
Example project demo, big group coding exercise on Module 1 materials |
| 3 | 2 | Module 2 exercises
Part 1 |
Small group exercise on Module 2 materials |
| 4 | 2 | Module 2 exercises
Part 2 |
Example project demo, big group exercise with Module 2 materials |
| 5 | 3 | Module 3 exercises
Part 1 |
Small group exercise on Module 3 materials |
| 6 | 4 | Module 3 exercises
Part 2 |
Big group exercise with Module 3 materials |
| 7 | 4 | Module 4 exercises
Part 1 |
Small group exercise with Module 4 materials |
| 8 | 4 | Module 4 exercises
Part 2 |
Big group exercise with Module 4 materials |
| 9 | 5 | Module 5 project
|
Small group exercise with Module 5 materials |
| 10 | 5 | Module 5 project
|
Big group exercise with Module 5 materials |
Module 1 - Basic operators, data types and structures, flow control
On completing this module, students will be able to write and execute simple numeric and string operations, compare values for equality or magnitude, store values in variables, and use basic flow control to tell your program when to do what.
|
Week 1
|
Week 2
|
Module 2 - Flow Control continued, common object methods
On completing this module, students will be able to perform more advanced comparisons of objects, take advantage of pythonic shortcuts for Boolean comparisons, and use some of the powerful functionality of two of the most used data structures in python: lists and dictionaries.
|
Week 3
|
Week 4
|
Module 3 - Writing and calling functions
On completing this module, students will be able to define and execute their own functions (like mini programs within a program) to easily apply the same code to similar tasks without repetition. Students will also be able to set default values for function inputs and use flow control internally within functions.
|
Week 5
|
Week 6
|
Module 4 - Using pandas for tabular data
On completing this module, students will be able to use pandas (python’s most popular library for working with tabular data) to read and write data from and to files, perform boolean and mathematical operations on rows and columns, filter data, assign new columns, and handle null values.
|
Week 7
|
Week 8
|
Module 5 - Financial modelling with pandas
On completing this module, students will be able to perform the fundamental data transformation and aggregation operations using pandas dataframes, and to use dataframes, numeric methods with numpy, and custom functions to perform a range of financial modelling tasks.
|
Week 9
|
Week 10
|
Note: Topics marked with * are less critical and can be skipped if students are short of time.
CPD
45 Hours
Presenter
|
|
Chris CrowCrow Advisory
Chris is an independent economist specializing in applied microeconomics & quantitative modelling. Whilst at PwC he provided internal training sessions on Python. He has worked on economic analysis, cost-benefit analysis, and policy evaluation projects in sectors including, transport, urban development, housing and land markets, water supply, forestry, financial services, and solid waste. |
Dates & Timings
Ten live online sessions commence in the first week of March 2025, with five sessions scheduled for prior to a break for Easter and School holidays, then recommences with another five sessions from May until June 2025. Exact timings will be announced late 2024 but the 90 minute online lectures will be held on Wednesday afternoons.
Each week has a commitment of at least the following: 1 x 90 minute live online lecture 1 x 60 minute live online small group session There will also be 2 hours of weekly homework that will need to be submitted to the course presenter for review and feedback.
It is expected that all participants understand the requirement to complete homework by the set deadlines and commit fully to their responsibilities.
Cost
Members: $1,975 + GST
Non-members: $2,400 + GST
10% discount applies where three or more register from the same organisation.
Contact Details and How to Register
Places are limited and will be provided on a first in first served basis.
Contact Faith Taylor at support@infinz.com to enquire.
Refund Terms and Conditions
- Tickets must be paid before the Masterclass takes place, otherwise your ticket is not guaranteed
- Tickets can be transferred to another person before the course commences (provided they are from the same charging group)
- Full refund if cancelled more than 28 days prior to Masterclass
- 50% refund if cancelled between 14 and 28 days prior
- 25% refund if cancelled between 7 and 14 days prior
- No refund if cancelled within 7 days of the event