Foundations of Probability and Statistics for Data Science

In today's data-driven world, understanding the mathematical principles behind data analysis is essential for any aspiring analyst or engineer. This text-based course guides you from foundational concepts of probability to practical statistical inference, helping you build a rigorous framework for decision-making. You will transition from basic theory to confident data interpretation through clear, structured explanations. By completing this course, you will understand how to model uncertainty, analyze variables, and draw valid conclusions from data. You will also learn how these classical concepts underpin modern predictive modeling and machine learning algorithms. What you'll learn: - Understand core probability theory, sample spaces, and conditional probability - Master random variables, probability distributions, and key expectation metrics - Apply central limit theorem concepts and sampling distributions to real-world data - Perform hypothesis testing and construct confidence intervals with precision - Analyze relationships between variables using linear regression models - Explore modern computational statistics basics, including bootstrap resampling methods This course begins with essential terminology, set theory, and foundational definitions before moving into complex distributions and inference techniques. Each section combines theoretical explanations with step-by-step mathematical breakdowns and practical scenarios. This course is designed for beginners, students, and aspiring data professionals. No advanced mathematical background is required to start. Begin your journey toward statistical literacy and unlock the power of data today.