# R Essentials for Data Science & Analytics

R Essentials for Data Science takes students currently working with Excel (or SAS or another data tool) for numerical analysis and want to get started using more powerful Open Source environments including the R programming language. R is a functional programming environment employed by many data analysts and data scientists, easily accessible to non-programmers and naturally extending a skill set that is common to data analysts and data scientists. It's the perfect tool for when the one has a statistical, numerical, or probabilities-based problem based on real data and they've pushed those tools past their limits. In this course we present common scenarios that are encountered in analysis and present practical solutions. Some attention is paid to data science theory including AI grouping theory. A discussion of using R with libraries are included and prepares the user for using Spark/R (and SparklyR).

#### Course Days: 2

Learning Objectives

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development focused on R and related tools. Working in a hands-on learning environment, led by our expert practitioner, students will learn R and its ecosystem, and where it’s a better a tool than Excel.

Students will explore:

• Moving from Excel to R
• R Basics
• Vectors
• Dates
• Multiple Dimensions
• Overview of R in Data Science

Audience & Pre-Requisites

This is an Introductory course, geared for Data Analyst and Data Scientists who need to learn the essentials of how to program in R. Incoming students should have prior experience working with Excel or SAS, and should know the basics of SQL.

1. From Excel to R
• Common problems with Excel
• The R Environment
• Hello, R
• CRAN

1. R Basics
• Simple Math with R
• Working with Vectors
• Functions
• Using Packages

1. Vectors
• Vector Properties
• Creating, Combining, and Iterating
• Passing and Returning Vectors in Functions
• Logical Vectors

• Text Manipulation
• Factors

1. Dates
• Working with Dates
• Date Formats and formatting
• Time Manipulation and Operations

1. Multiple Dimensions
• Indices and named rows and columns in a Matrix
• Matrix calculation
• n-Dimensional Arrays
• Data Frames
• Lists

1. Overview of R in Data Science
• AI Grouping Theory
• K-means
• Linear Regression
• Logistic Regression
• Elastic Net

1. Next Steps
• Powerful Data through Visualization: Communicating the Message
• R in Spark
• Demo(s)