Statistical Foundations for Data Analytics
covers fundamental concepts such as population, sample, probability, random variables, and their distributions, enabling students to grasp modern data analytics. The course also includes various exploratory data analysis and visualization techniques using specialized statistical software such as R. Additional topics include point and interval estimation, hypothesis testing for one or more populations, and a brief overview of correlation and simple linear regression.
Prerequisites & course information
CR: STAT 2550 , the former STAT 2510 LH: one 90 minute lab per week. Lab computations are done with appropriate statistical software such as R, although no prior statistics computing experience is assumed. PR: 3 credit hours in Mathematics or Statistics courses, or a combination of placement test and high school Mathematics scores acceptable to the School of Science and the Environment
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- Statistical Foundations for Data Analytics · Undergraduate