Statistics for Data Science I

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. Required for students pursuing a major in Data Science. Prerequisite: Multivariable Calculus (Math 233). No prior knowledge of Statistics is required. NOTE: Math/SDS 3211 and Math/SDS 3200 can not both count towards any major or minor in the Statistics and Data Science Department.
Course Attributes: FA NSM; AS NSM; AS AN

Statistics for Data Science I

STATISTICS AND DATA SCIENCE 3211

Section 01

Section 11