# Advanced Mathematics Learning Path

Created By Quantstart.com

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Top down, Task based education, Self-paced education

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# Advanced Mathematics Learning Path

I am often asked in emails how to go about learning the necessary mathematics for getting a job in quantitative finance or data science if it isn't possible to head to university. This article is a response to such emails. I want to discuss how you can become a mathematical autodidact using nothing but a range of relatively reasonably priced textbooks and resources on the internet. While it is far from easy to sustain the necessary effort to achieve such a task outside of a formal setting, it is possible with the resources (both paid and free) that are now available.

## Features

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## Main Modules

**Year1:Foundation**

**Year1:Real Analysis - Sequences and Series**

**Year1:Linear Algebra**

**Year1:Ordinary Differential Equations - Introduction**

**Year1:Geometry - Euclidean**

**Year1:Algebra - Group Theory**

**Year1: Probability**

**Year1: Mathematical Computing**

**Year2:Real Analysis - Riemann Integral**

**Year2:Metric Spaces**

**Year2:Vector Calculus**

**Year2:Ordinary Differential Equations - Non-linearity and Chaos**

**Year2:Geometry - Non-Euclidean**

**Year2:Abstract Algebra**

**Year2:Stochastic Processes**

**Year2:Numerical Analysis**

**Year2:Statistics**

**Year3:Complex Analysis**

**Year3:Topology**

**Year3:Ring Theory**

**Year3:Fluid Dynamics**

**Year3:Measure Theory**

**Year3:Linear Functional Analysis**

**Year3:Elementary Differential Geometry**

**Year3:Partial Differential Equations**

**Year3:Numerical Linear Algebra**

**Year4:Brownian Motion**

**Year4:Stochastic Analysis**

**Year4:Stochastic Calculus for Finance**

**Year4:Stochastic Optimal Control**

**Year4:Statistical Modeling**

**Year4:Statistical Machine Learning**

**Year4:Markov Chains**

**Year4:High Performance Computing**