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R Programming for Data Science

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Course Description


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.



After completing this course, students will use R to perform common data science tasks:
  • 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.


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 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.


Setting Up R and Executing Simple Code
  • Set Up the R Development Environment
  • Write R Statements
Processing Atomic Data Types
  • Process Characters
  • Process Numbers
  • Process Logicals
Processing Data Structures
  • Process Vectors
  • Process Factors
  • Process Data Frames
  • Subset Data Structures
Writing Conditional Statements and Loops
  • Write Conditional Statements
  • Write Loops
Structuring Code for Reuse
  • Define and Call Functions
  • Apply Loop Functions
  • Manage R Packages
Managing Data in R
  • Load Data
  • Save Data
  • Manipulate Data Frames Using Base R
  • Manipulate Data Frames Using dplyr
  • Handle Dates and Times
Analyzing Data in R
  • Examine Data
  • Explore the Underlying Distribution of Data
  • Identify Missing Values
Visualizing Data in R
  • Plot Data Using Base R Functions
  • Plot Data Using ggplot2
  • Format Plots in ggplot2
  • Create Combination Plots
Modeling Data in R
  • Create Statistical Models in R
  • Create Machine Learning Models in R
  • Appendix A: Handling Issues in Code
  • Appendix B: R Resources
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