Course Outline

Day One: Language Basics

  • Course Introduction
  • About Data Science
    • Data Science Definition for government applications
    • Process of Conducting Data Science in a governmental context
  • Introduction to the R Programming Language
  • Variables and Data Types
  • Control Structures (Loops and Conditionals)
  • R Scalars, Vectors, and Matrices
    • Defining R Vectors for data management tasks
    • Matrices for complex data structures
  • String and Text Manipulation
    • Character Data Type for government datasets
    • File Input/Output operations
  • Lists for organizing diverse data elements
  • Functions
    • Introduction to Functions in R
    • Closures for advanced programming techniques
    • lapply/sapply functions for efficient data processing
  • DataFrames for structured data representation
  • Laboratory Exercises for all Sections

Day Two: Intermediate R Programming

  • DataFrames and File I/O Operations
  • Reading Data from Various File Formats
  • Data Preparation Techniques for government datasets
  • Built-in Datasets for Practical Examples
  • Visualization
    • Graphics Package for visual data representation
    • plot(), barplot(), hist(), boxplot(), and scatter plot functions
    • Heat Maps for complex data visualization
    • ggplot2 package (qplot(), ggplot()) for advanced graphics
  • Data Exploration Using Dplyr for efficient data manipulation
  • Laboratory Exercises for all Sections

Requirements

  • A foundational understanding of programming is preferred.

Audience

  • Data analysts for government
 14 Hours

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