We're offering 20% off September Live Online classes! See which courses are applicable.   |   Details >

  
AccountIcon BigDataIcon BlogIcon default_resource_icon CartIcon checkmark_icon cloud_devops_icon computer_network_admin_icon cyber_security_icon gsa_schedule_icon human_resources_icon location_icon phone_icon plus_icon programming_software_icon project_management_icon redhat_linux_icon search_icon sonography_icon sql_database_icon webinar_icon

Search UMBC Training Centers

Big Data Analytics

R Programming

+ View more dates & times
    
    
    
    
                     
  • Overview

    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.

    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.

  • Who Should Take This Course

    AUDIENCE

    Business Analysts, Technical Managers, and Programmers.

    PREREQUISITES

    Participants should have the general knowledge of statistics and programming.

  • Why You Should Take This Course

    Topics covered include:

    • 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
  • Schedule
  • Course Outline

    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
  • FAQs
    Is there a discount available for current students?

    UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.

    What is the cancellation and refund policy?

    Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.

Contact Us