Statistical and Predictive Modeling for Finance

About this course

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.

Course objectives

• develop quantitative skills used in financial analysis
• gain hands-on experience with financial use cases
• recommend data-driven lending strategies based on model evaluations