This Probability and Statistics for Engineers course is a comprehensive program designed to build strong foundations in both probability theory and statistical methods used in engineering and applied sciences. The course provides a structured progression from basic concepts to advanced analytical techniques.

It begins with set theory, events, and the axioms of probability, helping learners understand how probability systems are defined and used. The course then introduces Bayes’ Theorem and discrete random variables, followed by a detailed study of both discrete and continuous random variables.

Students will explore joint probability distributions and learn how multiple variables interact in real-world systems. Advanced topics include transformations of random variables, the central limit theorem, and key statistical principles used in engineering analysis.

The course also covers estimation techniques such as the method of moments and maximum likelihood estimation (MLE), which are essential tools in statistical modeling and inference. In addition, learners are guided through exam-style problems to reinforce understanding and application.

This course is ideal for engineering students, data science learners, and anyone working in fields that require applied probability and statistics.

By the end of the course, learners will be able to confidently analyze random processes, apply statistical models, and solve engineering-related probability problems.