STAT 2870 A (CRN: 12818)
Statistics: Basics of Data Science
3 Credit Hours
For crosslists see: CS 2870 A
About STAT 2870 A
Basic data science techniques, from import to cleaning to visualizing and modeling, using the R language. Machine learning methods include regression, classification and clustering algorithms. Programming methods include user-defined functions. Prerequisites: STAT 1110, STAT 1410, STAT 2430, or STAT 3210. Cross-listed with: CS 2870.
Notes
Prereqs enforced by the system: STAT 1110 or STAT 1410 or STAT 2430 or STAT 3210; Open to Degree and PACE students; Crosslisted with CS 2870 A; Total combined enrollment: 56
Section Description
Basic data science techniques, from import to cleaning to visualizing and modeling, using the R language. Machine learning methods include regression, classification and clustering algorithms. Programming methods include user-defined functions.
Section Expectation
At the end of the course, students will be expected to know how to construct the appropriate visuals for data using the ggplot2 package, manipulate data frames using base R and the Tidyverse packages, write custom functions, perform loops, and build/improve models using machine learning techniques. No programming experience required.
Evaluation
Students will be assessed by homeworks, in-class quizzes, and a group project.
Important Dates
Note: These dates may change before registration begins.
Note: These dates may not be accurate for select courses during the Summer Session.
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