The Mathematics Primer covers the foundations which underpin the data science and quantitative finance courses.

• Linear algebra: vector spaces, linear operators, geometry, matrix decomposition, matrix operations
  
• Calculus: differentiation, Taylor expansion, integration, monotone and convex functions
  
• Optimization: smooth programming, convex programming, quadratic regularization, selection problems
  
• Statistics: representations of distributions, conditioning, normal, lognormal, binary mixture distributions

The Finance Primer covers the fundamental concepts on financial products (value, P&L, fixed income, derivatives) which underpin the quantitative finance courses

• Finance foundations: Value, transaction value, position of financial instruments; cashflows; market microstructure
  
• Peformance foundations for single instrument positions: payoff, holding and trading P&L, return
  
• Asset classes: equity, fixed income, derivatives
  
• Introduction to credit risk

The Python Primer covers the basics of coding in Python

• Basics of Python programming
  
• Control flow for decision making, loops for repetitive tasks and functions for simplifying calculations
  
• Numerical and linear algebraic computations, statistical data analysis, and data visualization
  
• Machine learning methods