Training

 

 

Popular Courses

Browse Our Free Resources

  • whitepapers
  • whitepapers
  • webinars
  • blogs

Our Locations

Training Centres

Vancouver, BC
Calgary, AB
Edmonton, AB
Toronto, ON
Ottawa, ON
Montreal, QC
Hunt Valley
Columbia

locations map

Calgary

550 6th Av SW
Suite 475
Calgary, AB
T2P 0S2

Toronto

821A Bloor Street West
Toronto, ON
M6G 1M1

Vancouver

409 Granville St
Suite 902
Vancouver, BC
V6C 1T2

U.S. Office

436 York Road
Suite 1
Jenkintown, PA
19046

Other Locations

Dallas, TX
Miami, FL

Home > Training > Big Data > R Programming Training

R Programming Training

Quick Enroll

Course#: WA2324

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.

OBJECTIVES

This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.

TOPICS

  • High octane introduction to R programming
  • Learning about R data structures
  • Working with R functions
  • Statistical data analysis with R
  • Supervised and unsupervised machine learning with R

AUDIENCE

Business Analysts, Technical Managers, and Programmers

PREREQUISITES

Participants should have the general knowledge of statistics and programming

DURATION

2 Days

Outline of WA2324 R Programming Training

CHAPTER 1. INTRODUCTION

  • Installing R
  • Character Terminal and GUI Interfaces to R
  • Other GUI Integrated Development Environments

CHAPTER 2. WORKING WITH R

  • Running R
  • Learning GUI Integrated Development Environment
  • Interacting with R Interpreter
  • R Sessions and Workspaces
  • Saving Your Workspace
  • Loading Your Workspace
  • Removing Objects in Workspace
  • Getting Help
  • Getting System Information
  • Standard R Packages
  • Loading Packages
  • CRAN (The Comprehensive R Archive Network)
  • Extending R

CHAPTER 3. R SYNTAX

  • General Notes on R Commands and Statements
  • Variables
  • Assignment Operators
  • Arithmetic Operators
  • Logical Operators

CHAPTER 4. R DATA STRUCTURES

  • R Objects
  • Vectors
  • Logical Vectors
  • Character Vectors
  • Creating and Working with Vectors
  • Lists
  • Creating and Working with Lists
  • Matrices
  • Creating and Working with Matrices
  • Data Frames
  • Creating and Working with Data Frames
  • Interactive Creation of Data Frames
  • Getting Info about a Data Frame
  • Sorting Data in Data Frames
  • Matrices vs Data Frames

CHAPTER 5. FUNCTIONS

  • Using R Common Functions
  • Numeric Functions
  • Character / String Functions
  • Date and Time Functions
  • Other Useful Functions
  • Applying Functions to Matrices and Data Frames
  • Type Conversion
  • Creating and Using User-Defined Functions

CHAPTER 6. CONTROL STATEMENTS

  • Conditional Execution
  • Repetitive Execution

CHAPTER 7. SCRIPTS

  • Creating Scripts
  • Loading and Executing Scripts
  • Batch Execution Mode

CHAPTER 8. INPUT / OUTPUT

  • Reading Data from Files
  • Writing Data to Files
  • Getting the List of Files in a Directory
  • Diverting System Output to a File

CHAPTER 9. DATA IMPORT AND EXPORT

  • Import and Export Operations in R
  • Working with CSV Files
  • Reading Data from Excel
  • Exporting Data in SPSS Data Format

CHAPTER 10. R STATISTICAL COMPUTING FEATURES

  • Basic Statistical Functions
  • Writing Your Own skew and kurtosis Functions
  • Generating Normally Distributed Random Numbers
  • Generating Uniformly Distributed Random Numbers
  • Using the summary() Function
  • Math Functions Used in Data Analysis
  • Correlations
  • Testing Correlation Coefficient for Significance
  • Regression Analysis
  • Types of Regression
  • Simple Linear Regression Model
  • Least-Squares Method (LSM)
  • LSM Assumptions
  • Fitting Linear Regression Models in R
  • Confidence Intervals for Model Parameters
  • Multiple Regression Analysis
  • Finding the Best-Fitting Regression Model
  • Comparing Regression Models with anova and AIC

CHAPTER 11. DATA VISUALIZATION

  • R Graphics
  • Graphics Export Options
  • Creating Bar Plots in R
  • Using barplot() with Matrices
  • Stacked vs Juxtaposed Layouts
  • Customizing Plots
  • Histograms
  • Building Histograms with hist()
  • Pie Charts
  • Generic X-Y Plotting
  • Dot Plots

CHAPTER 12. DATA SCIENCE ALGORITHMS AND ANALYTICAL METHODS

  • Supervised and Unsupervised Machine Learning Algorithms
  • k-Nearest Neighbors
  • Monte Carlo Simulation
Address Start Date End Date
Instructor Led Virtual 01/16/2018 01/17/2018
Instructor Led Virtual 02/05/2018 02/06/2018
We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC.
*Your name:

*Your e-mail:

*Phone:

*Company name:

Additional notes:

We have received your message. A sales representative will contact you soon.

Thank you!.

more details
buy this course

01/16/2018 - Online Virtual
$1,395.00
Enroll

02/05/2018 - Online Virtual
$1,395.00
Enroll

Register for a courseware sample

It's simple, and free.

 

Thank You!

You will receive an email shortly containing a link to download the requested sample of the labs for this course.