Introduction to R for Professionals is a two-day hands-on course that develops the skills required to use the R programming language for analyzing data and solving strategic issues. At the end of the course, participants will have developed a solid understanding of R data science fundamentals.

Teaching is done through a combination of theory, instructor-led examples, and problem sets. Partici- pants receive a wealth of example code, a quick reference guide, and a list of recommended resources for further study.

Learning Objectives

  1. Create R projects and scripts using RStudio.
  2. Understand the basics of R programming.
  3. See how R creates opportunities for better project management.
  4. Use R to efficiently combine, tidy, and explore data.
  5. Create high impact visualizations.
  6. Write machine learning algorithms.
  7. See examples of how to turn R Scripts into high quality reports (Word, pdf, slides, interactive reports).


Participants are not required to have any programming experience; however, they should possess basic computer skills (e.g Excel, Word). Participants are required to bring their own laptops.


Note: Although the standard course curriculum uses a simulated HR data set to guide its learning curve, the design patterns and methodologies taught in the course are applicable to a wide range of business analysis and strategic decision making.

Day Session Time Topic
Day 1 Morning 9:30am
  • Course introduction
  • Handout materials
  • Install and setup required programs
Day 1 Morning 9:45am
  • Overview of the data science process
  • What is R?
  • Advantages of R over spreadsheets and other languages
  • Exploring the R development environment
  • HR Attrition: What data do we have? How will we use the data science process to determine which factors are most important to employee attrition and how to predict which employees are most likely to leave?
Day 1 Morning 10:15am
  • Inputting Data into R
  • Examining R Data objects
  • Exploring data with the core R functions
Day 1 Morning 12:00pm
  • Lunch Break
Day 1 Afternoon 1:00pm
  • Conditional statements
  • Control statements
  • Creating your own functions
  • Exercise: Measuring employee performance with simulations.
Day 1 Afternoon 2:15pm
  • Advanced data manipulation
  • Exercise: Importing HR data
  • Exercise: 'Tidying' HR data
  • Exercise: Combining various HR data sets
  • Exercise: Quickly exploring HR data with the dplyr package
Day 1 Afternoon 4:30pm
  • End of Day 1
Day 2 Morning 9:30am
  • Recap of Day 1
Day 2 Morning 9:45am
  • Creating high impact graphics with R
  • How R makes visualizing data fast and flexible
  • Making interactive visualizations
  • Exercise: Visualize the relationship between various factors and attrition (e.g. barcharts, scatter plots, box plots, density plots, etc.)
  • Exercise: Create interactive visualizations
Day 2 Afternoon 12:00pm
  • Lunch Break
Day 2 Afternoon 1:00pm
  • Overview of popular machine learning algorithms
  • How to train and test models
  • Using decision tree algorithms for predictions and analysis
  • Exercise: Use machine learning algos to create a model which identifies the factors that are most important to employee attrition
  • Exercise: Use our model to predict which employees are most likely to leave the company
  • Reporting with R (Examples with RMarkdown and RShiny)
Day 2 Afternoon 4:30pm
    End of Day