Conditional Probability and Bayes' Theorem in R

Making data-driven decisions requires understanding how events influence one another. Probability is the foundation of data science, and mastering how to calculate likelihoods under specific conditions is essential for building predictive models. This course guides you through the core concepts of conditional probability and statistical independence using the R programming language. You will transition from basic probability rules to implementing a text classification algorithm, equipping you with the mathematical foundations and coding skills needed to analyze complex datasets. What you'll learn: - Understand fundamental probability concepts, including empirical, theoretical, and joint probabilities. - Apply counting techniques such as permutations and combinations to solve complex probability problems. - Calculate conditional probabilities and evaluate statistical independence using R. - Master Bayes' theorem to update probabilities based on new evidence or prior knowledge. - Build a functioning text classifier using the multinomial Naive Bayes algorithm in R. - Write clean, vectorized R code to process text data and compute probabilities efficiently. The course begins with core terminology and foundational definitions before moving into conditional probability formulas. Finally, you will apply these mathematical principles to clean, format, and classify text data in R. This course is designed for beginners in data science and statistics; no prior advanced math or R programming experience is required. Start reading today to build a strong statistical foundation for your data science journey.