Statistical Estimation and Inference for Data Science

Data science is more than just running algorithms; it requires understanding the certainty and reliability of your data. This course introduces the core concepts of statistical inference and estimation, helping you move from raw data to confident, mathematically backed conclusions. You will transition from calculating simple averages to constructing robust statistical models. By understanding how to define estimators, compute confidence intervals, and apply modern estimation techniques, you will gain the mathematical foundation necessary for advanced data analysis and machine learning. What you'll learn: - Understand foundational concepts of sampling distributions, parameters, and estimators. - Apply the Method of Moments and Maximum Likelihood Estimation to fit statistical models. - Construct and interpret confidence intervals to quantify uncertainty in your data. - Practice bootstrap resampling techniques to estimate distributions computationally. - Evaluate the bias, variance, and efficiency of different point estimators. The course begins with essential terminology and the basics of probability distributions before progressing to parameter estimation and interval construction. Through clear written explanations and step-by-step mathematical breakdowns, you will learn how to apply these theoretical concepts to real-world data scenarios. This course is designed for aspiring data scientists, analysts, and AI practitioners who want to build a strong mathematical foundation. No advanced prior background in statistical theory is required, though basic algebra is helpful. Start building your statistical foundation and make more reliable, data-driven decisions today.