R Essentials Primer for Data Science

QuickStart to R for data-focused skills covering R Basics, Vectors, Files, Dates, Multiple Dimensions & More



2 Days

Course Overview

R Essentials Primer for Data Science is a quick-start style, light hands-on course that takes students currently working with Excel (or SAS or another data tool) for numerical analysis and helps them 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).

NOTE: For deeper hands-on coverage or R Programming for Data Science please consider TT6683 JumpStart Hands-On R Programming for Data Science & Analytics, a variation of this course with more advanced hands-on and concepts.

Course Objectives

This course is approximately 40% hands-on lab to 60% lecture ratio, combining engaging lecture, demos, group activities and discussions with light machine-based practical programming labs and exercises.  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
  • Reading and Writing Files
  • Dates
  • Multiple Dimensions
  • Overview of R in Data Science

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be further adjusted to accommodate.  We offer additional R Programming, Python, data science, AI / machine learning / deep learning and other related topics that may be blended with this course for a track that best suits your needs. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

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.

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Follow On Courses: Our core R and Python programming, data science, analytics, AI and machine learning training courses provide students with a solid foundation for continued learning based on role, goals, or their areas of specialty.  Please inquire for next step recommendations based on your goals. 

Course Agenda

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

1. From Excel to R

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

2. R Basics

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

3. Vectors

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

4. Reading and Writing Files

  • Text Manipulation
  • Factors

5. Dates

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

6. Multiple Dimensions

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

7. Overview of R in Data Science

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

8. Next Steps

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

Course Materials

Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive related (as applicable) project files, code files, data sets and solutions required for the hands-on work.

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