Statistical and Predictive Modeling for Finance

Apply regression, statistical analysis, and supervised learning to evaluate financial performance and predict risk. In this course, you’ll build the quantitative skills used by financial analysts to interpret data and support investment and lending decisions. You’ll begin by calculating and interpreting alpha and beta using regression analysis. Then, you’ll examine the assumptions behind linear regression and test model reliability using residual analysis. You’ll apply descriptive statistics to summarize datasets and design A/B tests to measure financial impact. Finally, you’ll build supervised learning models, including decision trees, to predict financial risk and evaluate model accuracy. What makes this course unique is its focus on applied finance scenarios. Instead of abstract statistics, you’ll work with financial use cases such as portfolio measurement and credit risk classification. The course concludes with a portfolio-ready project where you evaluate credit risk models and recommend a lending strategy using data-driven insights

• Apply regression to interpret alpha, beta, and financial relationships
• Design A/B tests and evaluate statistical assumptions
• Build and assess predictive models for financial risk classification

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work