In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

In this course, you will use R to perform common data science tasks. You will:

Set up an R development environment and execute simple code.
Perform operations on atomic data types in R, including characters, numbers, and logicals.
Perform operations on data structures in R, including vectors, lists, and data frames.
Write conditional statements and loops.
Structure code for reuse with functions and packages.
Manage data by loading and saving datasets, manipulating data frames, and more.
Analyze data through exploratory analysis, statistical analysis, and more.
Create and format data visualizations using base R and ggplot2.
Create simple statistical models from data.

  • Lesson 1: Setting Up R and Executing Simple Code

  • Topic A: Set Up the R Development Environment
    Topic B: Write R Statements

  • Lesson 2: Processing Atomic Data Types

  • Topic A: Process Characters
    Topic B: Process Numbers
    Topic C: Process Logicals

  • Lesson 3: Processing Data Structures

  • Topic A: Process Vectors
    Topic B: Process Factors
    Topic C: Process Data Frames
    Topic D: Subset Data Structures

  • Lesson 4: Writing Conditional Statements and Loops

  • Topic A: Write Conditional Statements
    Topic B: Write Loops

  • Lesson 5: Structuring Code for Reuse

  • Topic A: Define and Call Functions
    Topic B: Apply Loop Functions
    Topic C: Manage R Packages

  • Lesson 6: Managing Data in R

  • Topic A: Load Data
    Topic B: Save Data
    Topic C: Manipulate Data Frames Using Base R
    Topic D: Manipulate Data Frames Using dplyr
    Topic E: Handle Dates and Times

  • Lesson 7: Analyzing Data in R

  • Topic A: Examine Data
    Topic B: Explore the Underlying Distribution of Data
    Topic C: Identify Missing Values

  • Lesson 8: Visualizing Data in R

  • Topic A: Plot Data Using Base R Functions
    Topic B: Plot Data Using ggplot2
    Topic C: Format Plots in ggplot2
    Topic D: Create Combination Plots

  • Lesson 9: Modeling Data in R

  • Topic A: Create Statistical Models in R
    Topic B: Create Machine Learning Models in R


This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.




To ensure your success in this course, you should be comfortable with basic computer programming concepts, including but not limited to: syntax, data types, conditional statements, loops, and functions. You can obtain this level of skills and knowledge by taking the Logical Operations Introduction to Programming with Python course. You should also have at least a high-level understanding of fundamental data science concepts, including but not limited to: data engineering, data analysis, data storage, data visualization, and statistics. You can obtain this level of knowledge by taking the CertNexus DSBIZ (Exam DSZ-110): Data Science for Business Professionals course.


Length: 5.0 days (40 hours)


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