Financial Statistics with Python: Statistical Methods, Regression, and Risk Analysis for Finance

Financial Statistics with Pythonis a practical guide to applying statistical methods in finance using Python.

Designed for analysts, finance professionals, students, and quantitative beginners, this book introduces the core statistical tools used to understand financial data, evaluate uncertainty, and support better analytical decisions. It focuses on clear explanations, structured workflows, and Python-based examples that connect statistical theory to real-world financial analysis.

Inside, readers will explore descriptive statistics, probability distributions, hypothesis testing, regression analysis, and risk measurement. The book shows how these methods can be used to examine returns, volatility, relationships between variables, model behavior, and interpret financial datasets with greater confidence.

Rather than treating statistics as abstract theory, this guide emphasizes practical application. Each topic is framed around analytical use cases relevant to finance, including performance analysis, market data exploration, factor relationships, portfolio risk, and decision support.

Whether you are building a foundation in quantitative finance, improving your Python analysis skills, or strengthening your statistical toolkit, Financial Statistics with Pythonprovides a structured starting point for working with financial data in a disciplined and evidence-based way.