Statistical Estimators for Data Analysis and Science

Accurate data analysis relies on making reliable inferences about entire populations from limited sample data. This written course provides a structured introduction to statistical estimators, equipping you with the foundational mathematical thinking required to infer unknown parameters with confidence. You will begin by clarifying essential probability foundations, random sampling, and standard notations before examining how estimators work in practice. Through step-by-step written explanations and practical analytical exercises, you will learn to construct, evaluate, and interpret key point and interval estimates for real-world datasets. What you'll learn: - Understand foundational statistical concepts, parameters, sample statistics, and core terminology - Evaluate key properties of estimators, including bias, variance, consistency, and efficiency - Calculate standard point estimators such as sample mean, sample variance, and sample proportions - Apply fundamental estimation methods including Method of Moments and Maximum Likelihood Estimation - Construct and interpret confidence intervals to quantify estimation uncertainty - Practice assessing estimator performance with modern resampling concepts like bootstrapping The course begins with fundamental definitions and sampling theory, advances through standard estimation derivations, and concludes with practical evaluation methods. This course is designed for beginners, aspiring data analysts, and software engineers with basic arithmetic familiarity and no prior advanced statistics background. Start reading today to master the mathematical principles behind statistical estimation.