Foundations of Statistical Inference for Data Science

Every data-driven decision relies on drawing accurate conclusions from limited data, but moving from raw numbers to reliable insights requires a solid grasp of statistical inference. This text-based course guides you from foundational probability theory to practical hypothesis testing, giving you the mathematical confidence to analyze datasets and validate your findings. You will learn to think like a statistician, understanding not just how to run statistical tests, but when to apply them and how to interpret the results correctly in real-world scenarios. What you'll learn: - Understand core probability concepts, random variables, and key probability distributions. - Estimate population parameters using confidence intervals and point estimation. - Perform hypothesis testing, including t-tests and chi-square tests, to validate data assumptions. - Apply modern resampling methods like bootstrapping to estimate uncertainty without rigid assumptions. - Analyze relationships between variables using linear regression and correlation analysis. - Interpret p-values and statistical significance correctly to avoid common data analysis pitfalls. The journey begins with essential terminology and probability foundations before moving systematically through estimation, hypothesis testing, and practical regression models. Designed specifically for beginners and aspiring data professionals, this course requires no prior background in advanced statistics. Start reading today to build a rigorous analytical foundation for your data science journey.