Applied Statistics and Data Visualization.

Catalog description

Students experience Big Data with an introduction to data analysis that focuses on applied statistics and visualization utilizing R, Python, and spreadsheets in a lab setting. Content and methods include sampling, descriptive statistics, probability, correlation, regression, binomial and normal distributions, confidence intervals and hypothesis testing, ANOVA, and Chi-square tests. Prerequisite(s): ASC 93 with a C or better or qualifying math placement score.