This course starts with an introduction to R that will be used to study and explore various features of data sets and summarize important features using R graphical tools. It also aims to provide theoretical tools to understand randomness through elementary probability and probability laws governing random variables and their interactions. It integrates analytical and computational tools to investigate statistical distributional properties of complex functions of data. The course lays the foundation for statistical inference and covers important estimation techniques and their properties. It also provides an introduction to more complex statistical inference concepts involving testing of hypotheses and interval estimation. Prerequisite: Multivariable Calculus (Math 233) and/or permission of the instructor. No prior knowledge of Statistics is required. NOTE: Math 3211 does not count as an upper level elective for a math or stats major.
Course Attributes: FA NSMAR NSMAS NSMAS AN
Section 01Statistics for Data Science I
INSTRUCTOR: LahiriView Course Listing