You will apply regression techniques to calculate and interpret a stock's beta. You’ll translate statistical output into practical investment insights and communicate findings clearly.

You will recognize the key assumptions underlying classical linear regression and understand why they matter for financial modeling reliability. You’ll explore how violations can affect forecast accuracy and credibility.

You will apply an OLS regression model and plot residuals to identify heteroscedasticity. You’ll interpret diagnostic outputs and assess whether your model meets statistical standards.

You will understand key measures of central tendency and determine when the mean or median is more appropriate, especially with skewed financial data. You’ll interpret summary statistics to support sound decision-making.

You will apply descriptive statistics to summarize key features of a dataset. You’ll calculate, visualize, and communicate data patterns clearly for professional audiences.

You will explain the difference between a null and an alternative hypothesis and understand their role in financial experimentation. You’ll connect hypothesis testing logic to risk-adjusted performance evaluation.

You will apply A/B testing principles to design an experiment measuring an algorithm’s impact on the Sharpe ratio. You’ll structure test plans that distinguish true improvement from random variation.

You will describe the standard workflow for developing and evaluating supervised learning models, from defining the predictive question to validating results. You’ll understand how structured workflows improve transparency and trust.

You will apply a decision tree model to predict a categorical outcome and report its accuracy. You’ll interpret model performance metrics and communicate findings in clear business language.

In this project, you will evaluate two predictive credit risk models—a logistic regression model and a decision tree classifier—using provided statistical outputs and performance metrics. You will interpret regression coefficients, assess statistical significance, evaluate model assumptions, and compare classification performance using accuracy, precision, and recall. You will also analyze confusion matrix results and interpret pilot A/B testing outcomes. Based on your analysis, you will recommend a lending strategy that balances predictive performance, financial risk exposure, and business priorities. This project simulates a real credit risk evaluation task performed by entry-level financial and risk analysts.