Course 1Foundations of Actuarial Computing & Financial Engineering
Build computational fluency in base Python, master vectorized numerical computation, and implement foundational financial math models (SOA Exam FM/P alignment).
For: Actuarial students and career switchers with zero-to-minimal prior coding experience.
-
Lesson 1
Python Fundamentals & Financial Logic
Types, floating-point money, f-strings, and conditionals. Lab: deductibles, payroll tax, tax brackets.
-
Lesson 2
Collections, Iteration & Mathematical Series
Lists, tuples, dicts, sets, and loops. Lab: geometric series, perpetuities, loan polynomials.
-
Lesson 3
Functional Programming & Core Math Applications
Functions, scope, type hints, exceptions, comprehensions. Lab: an interest-theory toolkit.
-
Lesson 4 · Part A
Object-Oriented Programming: Classes & Objects
Attributes, methods, properties, and special methods like __repr__ and __add__.
-
Lesson 4 · Part B
Lab: Bonds & Mortgages as Objects
A CouponBond with price, yield-to-maturity, and duration; a MortgageLoan with a full schedule.
-
Lesson 5
Vectorized Numerical Computing with NumPy
Arrays, views vs. copies, broadcasting, linear algebra. Lab: bond portfolios, cohort mortality, portfolio returns.
-
Lesson 6
Discrete Distributions & Stochastic Simulation
Seeded generators, Bernoulli, Binomial, Poisson. Lab: stop-loss pricing on 10,000 simulated lives.
-
Lesson 7
Continuous Distributions & Loss Generation
scipy.stats PDFs, CDFs, and PPFs; heavy tails. Lab: a driver profile and claims generator.
-
Lesson 8 · Part A
Modules, Docstrings & Tests
Package structure, imports, Google-style docstrings, and unit testing with pytest.
-
Lesson 8 · Part B
Capstone: Financial Simulation Engine
A Monte Carlo engine for a portfolio’s 1-year loss, with VaR, TVaR, validation, and scenarios.