Probability and Statistics for Decision Making

Overview

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Build a strong foundation in probability and statistics to analyze uncertainty, interpret data relationships, and support data-driven decision-making. Learn practical statistical concepts used in business, finance, analytics, and research. This course provides a structured introduction to probability and statistical analysis through clear explanations and practical examples. You’ll learn how probability helps quantify uncertainty, how random variables and probability distributions work, and how events interact through concepts such as mutually exclusive and independent events. As the course progresses, you’ll explore essential statistical measures including mean, variance, standard deviation, correlation, and covariance to better understand data behavior and relationships between variables. Practical examples such as dice probability, contingency tables, and distribution analysis help learners connect theory with real-world analytical thinking. You’ll also examine advanced concepts related to distribution shape, central moments, skewness, and estimation methods such as the Best Linear Unbiased Estimator (BLUE). These techniques form the foundation for statistical reasoning and quantitative analysis used in modern decision-making environments. What makes this course unique is its step-by-step approach that gradually builds confidence in probability and statistics while emphasizing practical interpretation rather than abstract theory. By the end of the course, you’ll be able to interpret uncertainty, analyze datasets, and apply statistical reasoning to support smarter analytical and business decisions.

Syllabus

Foundations of Probability

This module introduces the fundamental concepts of probability and random variables. Learners explore how uncertainty is quantified using probability, understand probability distributions, and examine how events interact through concepts such as mutually exclusive events and contingency tables.

Event Dependence & Statistical Basics

This module focuses on the relationship between events and introduces foundational statistical measures used to analyze data. Learners study independent events and explore key statistical metrics such as mean, variance, standard deviation, correlation, and covariance to understand data behavior and relationships.

Distributions, Moments & Estimation

This module explores advanced statistical concepts related to the shape and characteristics of distributions. Learners examine central moments, understand skewed distributions, and learn how estimation techniques such as the Best Linear Unbiased Estimator (BLUE) are used in statistical modeling.