# Instructor-Led TrainingIntroduction to R for Data Scientists

Language: R

## Overview

R is a functional programming environment for business analysts and data scientists. It's a language that many non-programmers can easily work with, naturally extending a skill set that is common to high-end Excel users. It's the perfect tool for when the analyst has a statistical, numerical, or probabilities-based problem based on real data and they've pushed Excel past its limits.

Geared for data scientists or engineers with potentially lightÂ technical background or experience, R for Data Scientists is a hands-on R course that explores common scenarios that are encountered in analysis, and presents practical solutions to those challenges. Throughout the course, special attention is paid to data science theory including AI grouping theory. A discussion of using R with AI libraries like Madlib is also included. Students who want additional topics and extended hands-on exposure might consider the 5-day extended version of this course, Mastering R for Data Scientists.

## Key Learning Areas

• Introduction to the R Environment
• Going from Excel to R
• Simple math with R
• How and when to use and apply vectors
• Manipulating text
• Formatting dates; manipulating time and operations
• How to work with multiple dimensions
• Working with R with Madlib / AI libraries
• Techniques in Data Visualization

## Course Outline

From Excel to R

• Common problems with Excel
• The R Environment
• Hello, R

R Basics

• Simple Math with R
• Working with Vectors
• Functions
• Using Packages

Vectors

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

• Text Manipulation
• Factors

Dates

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

Multiple Dimensions

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

R in Data Science

• AI Grouping Theory
• K-means
• Linear Regression
• Logistic Regression
• Elastic Net

• Importing and Exporting static Data (CSV, Excel)
• Using Libraries with CRAN
• Other libraries

Data Visualization

• Powerful Data through Visualization: Communicating the Message
• Techniques in Data Visualization
• Data Visualization Tools
• Examples

## Who Benefits

We will collaborate with you to design the best solution to ensure your needs are met, whether we customize the material, or devise a different educational path to help your team best prepare for this training.

While there are no specific technical prerequisites, students should have had prior exposure to working with statistics and probability, as well as good hands-on working knowledge of Excel.

## Prerequisites

While there are no specific technical prerequisites, students should have had prior exposure to working with statistics and probability, as well as good hands-on working knowledge of Excel.