AcademyAcademyPython that does real work in a finance team.
Python's integers and floats look similar but behave differently, and mixing them carelessly leads to silent errors in financial calculations. Learn how to handle numbers correctly, when to use Decimal for money, and the pitfalls that catch most practitioners.
Data types are the foundation of every Python calculation in finance. Learn the four types you use daily, how to convert between them, and how to spot the silent errors that break balance sheets.
Keywords are Python's reserved words that control your program's flow and structure. Learn to spot them, use them correctly, and avoid the common mistakes that block beginners.
Constants prevent silent bugs, make regulatory thresholds explicit, and transform magic numbers into readable code. Learn how to use them properly in finance calculations.
Variables are how you store and update positions in Python, just as you track them in a treasury system. Learn to assign, name, and update variables so your finance code is clear, maintainable, and reflects real positions.
Print is your window into what your code is actually doing. In finance work, where a single error cascades through reports, print is not optional—it's your primary tool for validating logic before it goes live.
Good comments signal assumptions and protect your code from breaking when someone else reads it. Learn when to comment, what to say, and how to build the habit so your code is safe to hand over and clear enough to audit.
Jupyter Notebook is where you write Python code, see the output immediately, and document your reasoning all in one place. For treasury and regulatory reporting, it is the tool between learning and production.
Jupyter Notebook is where finance practitioners solve real problems with Python. It gives you immediate feedback on your code, lets you document your logic as you work, and creates an audit trail that regulators and auditors actually want to see.