DATA 222 - Data Science

A broad introduction to the fast-growing field of data science within the broader context of statistics, including Data Handling, Visualization, Reproducibility, Predictive modeling, and Machine Learning. It will investigate the advantages and boundaries of traditional statistical inference, why traditional statistical inference techniques are sometimes not enough, and differences in rhetorical and philosophical models between the two. Ethical issues in analytics and related areas (big data, genomics, etc.) will be discussed throughout.

Credit(s): 3
Prerequisite(s): STAT 190 - Basic Statistics or STAT 290 - Statistics or STAT 370 - Probability.
Disciplinary Perspective(s):
None
Interconnecting Perspective(s):
None
University Graduation Requirement(s):
None