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R Primer for Data Scientists | A Technical Overview

Move from Excel to R! Learn R language basics, read and write files, apply basic data science principles and more

GK# 9200

Course Overview


R Essentials for Data Scientists takes students currently working with Excel, Matlab, Mathematica, and/or SAS for numerical analysis and want to get started using more powerful OpenSource 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).

NOTE: For deeper coverage or R Programming for Data Science please consider TT6682 R Programming for Data Scientists & Analysts, another version of this course with more advanced hands-on and concepts. This outline, agenda and topics list can also be adjusted to meet the needs or your team or organization.


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What You'll Learn


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

  • R Language and Mathematics
  • How to work with R Vectors
  • How to read and write data from files, and how to categorize data in factors
  • How to work with Dates and perform Date math
  • How to work with multiple dimensions and DataFrame essentials
  • Essential Data Science and how to use R with it
  • Visualization in R
  • How R can be used in Spark


Viewing outline for:

Virtual Classroom Live Outline

Lesson: From Excel to R

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

Lesson: R Basics

  • Simple Math with R
  • Working with Vectors
  • Functions
  • Comments and Code Structure
  • Using Packages

Lesson: Vectors

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

Lesson: Reading and Writing Files

  • Text Manipulation
  • Factors

Lesson: Dates

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

Lesson: Multiple Dimensions

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

Lesson: Overview of R in Data Science

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

Lesson: Next Steps

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


Viewing labs for:

Virtual Classroom Live Labs

This “skills-centric” course is about 50% hands-on lab and 50% lecture, designed to train attendees in core R programming and data analytics skills, coupling the most current, effective techniques with the soundest industry practices. Throughout the course students will be led through a series of progressively advanced topics, where each topic consists of lecture, group discussion, comprehensive hands-on lab exercises, and lab review.



Students should have attended the course(s) below, or should have basic skills in these areas:

  • Basic introduction to SQL

Who Should Attend


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 and should know the basics of SQL.

Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 2 day

Request this course in a different delivery format.