Python in Finance Curriculum

An easy-to-follow introduction to Python coding in finance that will quickly equip you with the coding skills you need to start writing useful code in Python at work.

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Curriculum

1 – Basics

  • Jupyter Notebook Introduction

  • Variables

  • Python Functions

  • Data Types

  • Operators (arithmetic, comparison, Boolean)

2 – Data Structures

  • Tuples

  • Lists

  • Sets

  • Indexing and Slicing

3 – Language Structures

  • Range Function

  • For Loop

  • While Loop

  • If Statement

  • User-defined Functions

4 – Advanced Python Features

  • List Comprehension

  • Lambdas

5 – Pandas for Data Analysis

  • Dataframe Basics

  • Data Import/Export

  • Indexing, and Slicing Data

  • Access Methods

  • Date Columns, and Arithmetic

  • Data Manipulation

  • Handling Missing Values

  • Delimited Data

  • Merging Dataframes

  • Dataframe Arithmetic Operations

  • Data Aggregation

6 – Visualisation using Matplotlib

  • Line Chart

  • Bar Chart

  • Histogram

  • 3D Plotting

7 – Basic Financial Calculations with Pandas

  • Moneyness Computation

  • Forward Filling, Backward Filling

  • Linear Interpolation Using Specific Index

8 – Financial computing with NumPy, and SciPy

  • Arrays

  • Arrays Mathematics

  • Array Operations

  • Indexing, and Slicing

  • Black Scholes Option Pricer

  • Monte Carlo Pricing (Pure Python)

  • Monte Carlo Pricing (NumPy)

  • Sensitivities (Greeks) Computation

  • Vectorised Black Scholes Option Pricer

  • Implied Volatility Surface Generation

  • Value at Risk (VaR) estimation

  • Newton Iteration

  • Altman Iteration

9 – Project (for Full Program students only)

At the end of the cohort, students will build Python programs with financial applications, using the skills acquired during the course.