Foundations of Classification and Experimental Design

Making critical business and scientific decisions requires more than just collecting data; it demands structured experiments and accurate classification models. This text-based course guides you through the foundational principles of setting up planned experiments and analyzing classification outcomes. You will transition from simply observing data to actively designing trials, comparing regression and classification approaches, and applying statistical frameworks to evaluate your findings. What you will learn: Understand the fundamental differences between regression models and classification techniques; Design structured experiments using principles of randomization, replication, and blocking; Analyze experimental results using analysis of variance and hypothesis testing; Evaluate classification model performance using confusion matrices, precision, recall, and ROC analysis; Apply modern experimental frameworks, including basic A/B testing methodologies and sequential testing concepts; Formulate data-driven conclusions that minimize bias and maximize statistical power. The course begins with essential terminology and statistical foundations before walking you through classical experimental designs and modern classification metrics. You will progress through structured text lessons and practical scenarios that reinforce how to set up, execute, and analyze your own statistical tests. Designed for beginners, this course requires no advanced mathematical background, making it ideal for aspiring data analysts, researchers, and product managers. Start building your experimental toolkit and make more confident, data-backed decisions today.