Statistical Methods for Data Science

Welcome to the Ball State University course “Statistical Methods for Data Science.” As the title suggests, this course provides fundamental concepts and methods for data-generating mechanisms such as probability models and inferential methods such as estimation and hypothesis testing. scientists. You will need the right tools and analytics methods to make good sense of data and to make data-driven decisions. We are going to take a systematic approach to build a strong foundation on probability and probability models, large sample theory as a bridge between probability theory and inference, and basic inferential processes. Please 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 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

• Apply probability and distribution theory to data science problems
• Classify random variables and corresponding distributions
• Explain properties of discrete and continuous random variables
• Understand sampling distributions of sample statistics
• Choose suitable inference methods like estimation and hypothesis testing
• Use R environment for statistical simulation and computing

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work