DATA SCIENCE WITH R
Perform Data Science as an iterative (back and forth) process of four different types of activities (data collection, data wrangling, data analysis, communication). Traverse through the five requisite stances (scientist, coder, mathematician, methodologist, skeptic). Develop and hone a broad set of computational tools in R (but not the broadest) and a broad set of statistical/machine learning tools (but not the broadest). Focus on doing these with agility to make the coding "transparent" and serve the large goals of the project.
Prerequisites & course information
Requisites: ( STAT 333 or 340 ) and ( MATH 320 , 340 , 341 , 345 , or 375 ), graduate/professional standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2022 Learning Outcomes: 1. Describe and apply the key steps taken in most data science projects and how these steps fit into a coherent whole Audience: Undergraduate 2. Identify how a data set can be used for a specific purpose Audience: Undergraduate 3. Clean and analyze data Audience: Undergraduate 4. Communicate the results of data analysis Audience: Undergraduate 5. Develop agile and reproducible code that enables iterative development of a data science project using the tools in the tidyverse Audience: Undergraduate Learning Outcomes: 1. Describe and apply the key steps taken in most data science projects and how these steps fit into a coherent whole Audience: Undergraduate 2. Identify how a data set can be used for a specific purpose Audience: Undergraduate 3. Clean and analyze data Audience: Undergraduate 4. Communicate the results of data analysis Audience: Undergraduate 5. Develop agile and reproducible code that enables iterative development of a data science project using the tools in the tidyverse Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027