Practical Statistical Inference and Hypothesis Testing for Data Science

Making data-driven decisions requires more than just calculating averages; you need to know if your findings are statistically significant or just random noise. This course introduces the core principles of statistical inference and hypothesis testing, specifically tailored for modern data science applications. You will transition from simply looking at data to confidently drawing mathematically sound conclusions. By learning how to structure hypotheses, calculate test statistics, and correctly interpret p-values, you will gain the analytical rigor needed to validate A/B tests, evaluate experimental features, and avoid common statistical pitfalls. What you'll learn: - Understand foundational concepts of statistical inference, including populations, samples, and sampling distributions. - Formulate null and alternative hypotheses for real-world data science scenarios. - Calculate and interpret p-values, confidence intervals, and effect sizes correctly to avoid common misinterpretations. - Apply classical parametric tests such as t-tests, ANOVA, and chi-square tests using Python's scientific libraries. - Implement simulation-based inference, including permutation tests and bootstrapping, for non-standard data distributions. - Analyze statistical power and sample size requirements to design robust experiments and A/B tests. - Recognize and prevent common ethical and practical pitfalls, such as p-hacking and multiple comparison bias. The course begins with essential terminology and the logic of statistical probability before guiding you through hands-on, text-based calculations and Python code snippets for both classical and modern simulation-based tests. It is designed for beginner data analysts, aspiring data scientists, and developers looking to build a strong quantitative foundation with no prior advanced statistics background required. Start making reliable, statistically backed decisions with your data today.