Introductory probability, descriptive statistics, sampling distributions, parameter estimation, tests of hypotheses, chi-square tests, regression analysis, analysis of variance, and nonparametric tests.

ASU Catalog - STP 420

Example Syllabus (this may not be the syllabus used by your instructor, but gives students an idea of the course content and expectations).

Course Coordinator: Raymond Ye Zhang

Focuses on the exploration of the types of data typically encountered in modern data science, such as text data, spatial data, and time series data. Uses various statistical techniques to gain insight into the structure of the data, including graphical visualization, linear regression, trees and clustering.

Enroll requirements
Prerequisites: Prerequisite(s): MAT 266 or 271 with C or better. Credit is allowed only for ACT 370 or DAT 301 or STP 494 (Exploring Data in R and Python) or Visiting University Student

Examines quantitative literacy from a data and evidence driven perspective. Looks at the literature behind vaccines, climate, and other contentious topics where there is a wealth of scientific literature and yet these areas are still hotly debated. Investigates ways in which data science is abused; how to mislead with statistics, and how these problems have created a lack of trust in science.