Computational Statistics and Bayesian Inference for Data Science

Modern data science requires more than just running pre-packaged machine learning algorithms; it demands a deep, computational understanding of the underlying statistics. This text-based course bridges the gap between theoretical statistics and practical code, showing you how to simulate, analyze, and model complex data. You will transition from relying on black-box libraries to writing custom statistical simulations and performing practical Bayesian inference to make data-driven decisions with confidence. What you'll learn: - Understand foundational statistical concepts, probability distributions, and computational sampling methods. - Implement Monte Carlo simulations to estimate probabilities and model uncertainty. - Apply Bayesian inference techniques to update beliefs and estimate parameters using modern Python libraries. - Analyze model diagnostics and evaluate convergence using current statistical workflows. - Scale statistical computations to handle larger datasets efficiently. The course begins with core statistical definitions and probability basics before guiding you through hands-on simulation techniques and Bayesian modeling workflows. You will progress from simple sampling exercises to writing robust, reproducible statistical analyses. This course is designed for beginner data scientists, analysts, and programmers who want to build a strong statistical foundation. No prior advanced statistics background is required, though basic Python familiarity is helpful. Start mastering computational statistics and bring rigorous analytical depth to your data science projects today.