Data Science with R Programming

Our Professional Program in Data Science course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

We’ll cover R’s functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You’ll learn how to apply general programming features like “if-else,” and “for loop” commands, and how to wrangle, analyze and visualize data.

What you’ll learn

• Understand critical programming language concepts
• Make use of R loop functions and debugging tools
• Configure statistical programming software
• Collect detailed information using R profiler

Course description

Rather than covering every R skill you might need, you’ll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.

Basic R syntax

Foundational R programming concepts such as data types, vectors arithmetic, and indexing

How to perform operations in R including sorting, data wrangling using dplyr, and making plots

Course Outline

• R Studio walkthrough
• Download Resources
• Datatypes
• Data Structures
• Create group of elements in a vector
• Use repetitions and sequence to create a vector fast
• Random numbers, rounding and sampling
• Formatting numbers
• Approaches to filtering data
• Revenue impact of Ad-campaign
• Set Operations
• Checking existence
• Nested if-else
• Writing smarter For loops
• Memory pre-allocation tactics
• Working with lubridate and anytime
• Introduction to lists
• Named list, unlist and more
• Creating Dataframe
• Visual editing
• Various dataframe operations
• R datasets, packages and public datasets
• Conditional filtering and missing values
• Grouping and case problem solution
• Debugging techniques
• Error handling
• Steps involved in significance tests
• ANOVA – R demo