Quantitative Foundations
Statistics, probability, and finance math. The quantitative skills that underpin data science and finance.
Data science and finance are built on quantitative foundations. This section covers essential concepts: probability theory, statistical distributions, hypothesis testing, regression, and the finance-specific math you'll need for risk, portfolio analysis, and valuation.
Why These Foundations Matter
Every algorithm in data science and every model in finance has mathematical underpinnings. Understanding probability helps you reason about uncertainty. Statistics lets you draw valid conclusions from data. Finance math gives you the language of risk, return, and valuation. These aren't abstract concepts - they're the tools you'll use daily to analyze data, build models, and make decisions.
What You'll Learn
The courses here progress from descriptive statistics through probability, distributions, sampling, hypothesis testing, and regression. You'll also learn finance-specific quantitative concepts: portfolio math, volatility modeling, Value at Risk theory, and credit risk fundamentals. This foundation makes everything that follows in machine learning and applied finance more accessible.
Connecting to Finance
Finance is inherently quantitative. Risk is measured with standard deviations and VaR. Portfolio optimization uses covariance matrices. Options are priced with probability distributions. Trading strategies are validated through hypothesis testing. The examples throughout connect mathematical concepts to how they're actually used in finance work.
Guides
In-depth articles to help you understand the landscape and plan your learning.
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