Welcome to the Ball State University course “Statistical Methods for Data Science.” This course is about Statistical Methods for data scientists. To make good sense of data, you will need the right tools and analytics methods. We are going to take a systematic approach to learn about the right tools and methods you can use. Note that as data scientists it is important for us to be able to connect data and learn how the world around us works. To accomplish this challenging task, we will learn how we can connect data through probability theory and statistical models and take actionable decisions, confirm a hypothesis, or make predictions.

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 the 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.