Dec 26, 2024  
2020-2021 Catalog 
    
2020-2021 Catalog [ARCHIVED CATALOG]

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DAT 204 - R for Analytics


Credits: 3
3 Skills Lab Hours

Prerequisites: DAT 102   and MAT 120  

 
Description
R for Analytics will guide students to the use of the R software platform for data analysis.  After exploring the fundamentals of the R scripting language and essential data structures, students will learn to manipulate structured data in R in preparation for statistical analysis.  Standard statistical data analysis techniques will be implemented in R such as means testing, variable correlations and linear regressions.  In addition to mechanical fundamentals, this course is rooted in building skills in sound data analysis thinking:  surveying data sets, generating compelling inquiry questions, conducting rigorous quantitative analysis, drawing conclusions rooted in reproducible findings and discussing the limitations of this analysis with a lay audience.


Learning Outcomes
Upon successful completion of the course, the student will:

  1. Execute essential operations in R-Studio including loading and viewing structured data files, reading data into R objects and manipulating data.
  2. Choose appropriate data storage objects and configure them for desired analysis processes given structured data in a variety of formats.
  3. Generate basic plots and descriptive statistics in R for all relevant variables in a given structured data set.
  4. Explain the fundamentals of linear regression analysis and write R scripts to conduct a straightforward regression analysis on a structured data set.
  5. Implement an end-to-end data analysis process.
  6. Generate exportable plots and reports using Shiny.
Listed Topics
  1. Integrated development environment
  2. Structured data
  3. Vectors
  4. Objects
  5. Arrays and matrices
  6. Data frames
  7. Functions
  8. Regressions
  9. Plots
Reference Materials
The R software package is one of the most rigorously documented open-source data analysis systems available today. As such ample free online resources exist for both learning R fundamentals and conducting advanced analysis.
Approved By: Bullock, Quintin Date Approved: 04/25/2018


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