R Programming for Data Scientists and Analysts

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 you have a statistical, numerical, or probabilities problems based on real data and you’ve pushed Excel past its limits.

This comprehensive hands-on course presents common scenarios encountered in analysis and shares practical solutions. Special attention is paid to data science theory including AI grouping theory. A discussion of using R with AI libraries like MADlib is included.

    Dec 9 2020

    December 9 - 11, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live

    Date: 12/09/2020 - 12/11/2020 (Wednesday - Friday) | 10:00 AM - 6:00 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

    Success! Your message has been sent to us.
    Error! There was an error sending your message.

    REQUEST MORE INFO:


    R Programming for Data Scientists and Analysts

    December 9 - 11, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live


    How Did You Hear of Global IT Training?

    Join Our Email List?

From Excel or SAS to R

  • Common challenges with Excel/SAS
  • The R Environment
  • Hello, R

Working with R Studio

  • Rshiny
  • Rpresentations
  • Rmarkdown

R Basics

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

Vectors

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

Reading and Writing

  • Text Manipulation
  • Factors

Dates

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

Multiple Dimensions

  • Adding a second dimension
  • 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

R with MADLib
Importing and Exporting static Data (CSV and Excel)
Using Libraries with CRAN
K-means with MADlib
Regression with MADlib
Other libraries

Data Visualization

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

Databases, Data lakes, and additional Topics

  • Building connections to Databases and Data lakes, for both Python and R (using Hive server)
  • Methods to “query” data from database and data lakes, for both Python and R
  • Creating and passing macro variables.

R with Hadoop

  • Overview of Hadoop
  • Overview of Distributed Databases
  • Overview of Pig
  • Overview of Mahout
  • Exploiting Hadoop clusters with R
  • Hadoop, Mahout, and R

Business Rule Systems

  • Rule Systems in the Enterprise
  • Enterprise Service Busses
  • Drools
  • Using R with Drools

Join an engaging hands-on learning environment, where you’ll learn:

  • 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

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

 

Before attending this course, you should have:

  • Experience working with Excel or SAS
  • Understand SQL basics

 

Data Scientist, Data Analyst, Data Architect, Statistician, Data Engineer, Developer, and Database Administrators who need to leverage R for analytics.

Ready to Advance Your Career?

CONTACT US NOW!