Probability for improved business decisions: Introduction, Combinatorics, Bayesian Inference, Distributions
Instructor365 CareersThe curriculum covers a wide array of statistical distributions, including Uniform, Bernoulli, Binomial, Poisson, Normal, Student's T, Chi-Squared, Exponential, and Logistic.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
An instructor presents a visual hierarchy showing how probability forms the foundation for statistics and data science.
An instructor presents a slide outlining key course topics including practical examples, fundamental concepts, and an introduction to Bayesian probability.
An instructor presents a slide titled 'Necessary Preparation for Advanced Statistics, ML and DL,' highlighting the foundational role of this course in data science education.
Instruction includes frequent knowledge checks through quizzes paired with video lectures to reinforce topics like expected values and permutations.
This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected. The content is accessible to very young learners, which may result in a lack of technical depth for more advanced students.
One signal suggests that specific examples can be mentioned without sufficient clarification, and quiz questions may occasionally feel designed to trick rather than teach. Note that these signals were recorded before the displayed update date; while an update occurred later, it does not prove these issues were corrected.
