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.



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 – Python Projects (Financial Applications)

  • Residual Risk Add On (RRAO) capital

  • PnL Attribution Test (PLAT) – Spearman correlation, and Kolmogorov-Smirnov test

  • Scenario Generation
  • Sensitivities Based Method (SBM)

  • Risk Factor Eligibility Test

  • Value at Risk
  • Expected Shortfall
  • Timeseries imputation

  • Vanilla option pricing

  • Exotic option pricing
  • Stress testing

  • Back testing
  • Volatility surface construction
  • Monte Carlo stock price simulation (geometric brownian motion)

10 – Project (for FinBA students only)

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

Our Alumni Get Great Jobs

Graduates of our training programs secure employment with some of the most prestigious financial employers in locations across the globe. Many financial institutions sponsor their employees. More...

Become a highly skilled, highly paid professional in investment banking

Whether you’re new to finance/coding, adding more skills, or advancing your career, our training programs will prepare you for your ideal job.

Focused curricula

Each of our courses focuses on delivering only the specific knowledge you need to pass the toughest interviews, land your coveted role, and succeed in your career.

Learn from experts

Our courses are developed/taught directly by industry-leading implementation experts and thought leaders.

No finance or coding experience required

Beginner, novice or intermediate, whatever your level, you don’t need to have prior knowledge of finance or programming as all our courses are taught from foundations.

Practical learning

You won't just study the theory. Our training programs are heavily practical by design. You will learn by doing - by undertaking the type of work you will likely be doing on the job.

Coding projects

You will learn the highly sought-after programming languages of Python & SQL and then actually write the code needed to build 10+ prototypes of the kind of systems every investment bank uses.

Live teaching sessions

You will join a live web session every week where you can talk directly to the expert instructor, and other students for one-to-one and group support.


You will overcome the catch-22 hurdle of eligibility by earning a globally recognised official certificate from Financial Risk Hub that demonstrates your competence to potential employers.

CPD credits - CFA, FRM, & PRM

You can consolidate your professional quantitative finance & risk analytics credentials by earning credits from leading industry organisations such as the CFA Institute, GARP and PRMIA.

Professional recruiters

You will gain exclusive access to specialist investment banking recruiters to help you advance your career and land a high-paying role.

Go from Job Search to Job Success

We provide access to a wide variety of career services and support to help you secure your next role after successfully completing the program.

CV/resume help (FinBA program only)

You will be assigned a dedicated professional investment banking recruiter, specialising in Risk & Quant Finance, who will work 1-1 with you to tailor your existing CV/resume.

Leverage our network (FinBA program only)

We foster personal introductions to our network of potential hiring managers and recruiters, to help our students based in London, Toronto, NY/Boston/Chicago/Dallas, and Sydney/Melbourne get connected to the right people.

Job search support

You will receive invaluable guidance on how to navigate the complex recruitment landscape and conduct a smart search for your first or next role in investment banking.

Interview prep

You will be provided a comprehensive set of common interview questions and framework for how to answer them. In addition, we will train you on the mindset and approach needed to ace interviews at any investment bank.

Our Taught Courses

Gain in-demand, specialised knowledge on financial products and risk analytics on a hands-on course with an opportunity to qualify for GARP/PRMIA/CFA Institute CPD credits.
Learn one of the most popular programming languages in the financial industry - highly sought-after by employers.
Learn one of the most important programming languages in finance - Structured Query Language (SQL) for data storage, analysis and manipulation.
Get a solid introduction to counterparty credit risk, one of the key financial risks an investment bank faces, in a session led by Professor Johannes Ruf (London School of Economics).