Statistical Inference and Modeling for Data Science

Raw data is only valuable if you can extract meaningful, reliable insights from it. This course guides you through the core statistical principles needed to make accurate predictions and draw valid conclusions from your datasets. You will transition from simply looking at numbers to constructing robust statistical models and performing rigorous hypothesis tests. By understanding the mathematical foundations and applying them through modern data analysis workflows, you will gain the confidence to make data-driven decisions that stand up to scientific scrutiny. What you'll learn: Understand the foundational concepts of probability, parameters, and estimates; Configure and conduct hypothesis tests to determine statistical significance; Build and interpret linear and logistic regression models to identify key trends; Practice modern resampling techniques like bootstrapping to estimate uncertainty; Evaluate model fit and prevent common statistical pitfalls like overfitting. The course begins with essential terminology and probability theory before moving systematically into estimation, hypothesis testing, and regression modeling. You will learn through clear written explanations, practical code examples, and structured exercises designed to reinforce your analytical skills. Designed for aspiring data analysts, beginner data scientists, and anyone looking to build a strong quantitative foundation. Start mastering the science of data-driven decision-making today.