STAT 315 - Exploring and Analyzing Data

Units: 3
Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Introduction to statistical programming and statistical inference with hands-on analysis of real-world datasets, including economic data, document collections, and social networks. Lecture Units: 2; Activity Units: 1.

Prerequisites: Completion of GE area B4.
Possible Instructional Methods: Entirely On-ground.
Grading: A-F or CR/NC (student choice).
Course Typically Offered: Spring ONLY

Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply basic computational skills in descriptive statistics and data visualization, hypothesis testing, confidence intervals, modeling and error analysis, including the use of large data sets.
Analyze data using appropriate software, including cloud-based software, and to interpret results covering descriptive statistics and data visualization, hypothesis testing, confidence intervals, modeling and error analysis, including the use of large data sets.
Communicate results involving descriptive statistics and data visualization, hypothesis testing, confidence intervals, modeling and error analysis using reproducible research best practices.