Skills you'll gain

There are 5 modules in this course

After completing the course, you will be able to: 1) Apply probability and distribution theory to address real-world problems related to the data science field. 2) Classify the type of random variables and their probability distributions used to model various types of data in practice. 3) Outline the properties of discrete and continuous random variables. 4) Explain the sampling distributions of sample statistics such as the sample mean and the sample proportion. 5) Explain the Laws for Large numbers for the sample mean and the sample proportion. 6) Choose and use appropriate inference strategies, such as the right estimation method or the hypothesis test, to make inferences on unknown population parameters. 7) Illustrate the estimation process and hypothesis testing as a mode of statistical inference. 8) Outline multivariate discrete and continuous distributions to understand the joint behavior of several correlated discrete and continuous variables, respectively. 9) Relate multivariate analysis techniques to dimension reduction problems. 10) Utilize the R computational environment for probability simulation and other statistical computing in this course.

Random Variables and Their Properties

Discrete Parametric Family of Distributions, Part 1

Continuous Probability Distributions - Part I

Role of Normal Distribution in Statistical Inference

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