Data Analysis Tutorials

Learn the difference between exogenous and endogenous variables, why endogeneity matters for causal inference and regression, and how to identify and address endogenous variables.

Discover how Spearman's Rank Correlation captures relationships that curve, plateau, or shift pace instead of following a straight line. Learn to calculate, interpret, and apply it in Python, R, and Excel.

Learn what a residual plot is, how to interpret common patterns, and how residual plots help identify nonlinearity, heteroscedasticity, outliers, and other regression problems.

Compare stock market data APIs for backtesting, dashboards, screeners, and AI agents, with Python examples covering splits, reliability, and fundamentals.

Learn how to use VLOOKUP() in Google Sheets to search, find, and pull data from large datasets in seconds.

Learn how to create and use pivot tables in Google Sheets to summarize, analyze, and explore your data.

Spin up a no-cost BigQuery environment, practice real-world SQL on public and private data, and build dashboards while staying within generous free-tier limits.

Accuracy isn't enough. Learn the difference between precision and recall, understand the trade-off, and choose the right metric for your model.

Master Gurobi optimization from setup to real-world applications. Learn to build models, configure solvers, and tackle linear, integer, and nonlinear programming problems efficiently.

Learn to analyze categorical relationships when your sample sizes are small.

Learn the mathematical foundations of z-scores, explore practical calculation methods, and discover applications across statistics and data science.

Learn how to calculate and interpret the likelihood of multiple events occurring simultaneously. Discover practical applications in predictive modeling, risk assessment, and machine learning that solve complex data science challenges.