This course is designed to provide a general introduction to the rapidly growing field of data science. Topics will include data summarization and visualization, data distributions, the scientific and statistical inference process, confidence intervals, hypothesis testing, sampling, regression, and classification. The course will be very hands-on with students actively carrying out the techniques and procedures being taught on real datasets in order to foster the ability to reason with data. As part of this process, students will be taught the basics of programming (coding) in R and these skills will be reinforced through weekly lab sessions. In developing the core concepts, students will also be exposed to ancillary topics such as data ethics, simulation basics, and best practices in programming. No previous programming experience nor any background in statistics will be assumed.

Academic Career: Undergraduate
Course Component: Lecture
Grade Component: LG/SNC Elective Basis
Minimum Credits: 4
Maximum Credits: 4