R Programming for Data Scientists & Analysts

Course Features

Course Details

Overview

Comprehensive R Programming for Data Scientists & Analysts introduces participants to some of the most commonly seen scenarios along with solutions to challenges encountered when performing analyses. The course also covers data science theory with AI grouping theory. As a part of the program, participants will participate in discussions on using R with AI libraries such as Madlib.

Curriculum

From Excel to R

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

R Basics

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

Vectors

•   Vector Properties
•   Creating, Combining, and Iteratorating
•   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-meansa
•   Linear Regression
•   Logistic Regression
•   Elastic Net

R with MadLib

•   Importing and Exporting static Data (CSV, 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

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

This course does not have any sections.

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