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
Practice Exams
Test 1
Test 2
Test 3
Test 4
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: Sabiha Mahzabeen