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Data Analytics With R

Duration: 3 Days
Course Price: $2,195

Overview

The use of the R Programming language has become very popular open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data.

Audience

Developers / Data Analysts

Pre-requisites

· Basic programming background is preferred

Objectives

· R language fundamentals · R data structures (Lists, Dataframes, Matrices) · Graphing with R · Advanced analytics With R

Overview

The use of the R Programming language has become very popular open source environment for statistical computing, data analytics and graphics. This course introduces R programming language to students. It covers language fundamentals, libraries and advanced concepts. Advanced data analytics and graphing with real world data.

Audience

Developers / Data Analysts

Pre-requisites

· Basic programming background is preferred

Objectives

· R language fundamentals · R data structures (Lists, Dataframes, Matrices) · Graphing with R · Advanced analytics With R

Outline

Day One: Language Basics

· Course Introduction

· About Data Science

§ Data Science Definition

§ Process of Doing Data Science.

· Introducing R Language

· Variables and Types

· Control Structures (Loops / Conditionals)

· R Scalars, Vectors, and Matrices

§ Defining R Vectors

§ Matricies

· String and Text Manipulation

§ Character data type

§ File IO

· Lists

· Functions

§ Introducing Functions

§ Closures

§ lapply/sapply functions

· DataFrames

· Labs for all sections

 

Day Two: Intermediate R Programming

· DataFrames and File I/O

· Reading data from files

· Data Preparation

· Built-in Datasets

· Visualization

§ Graphics Package

§ plot() / barplot() / hist() / boxplot() / scatter plot

§ Heat Map

§ ggplot2 package ( qplot(), ggplot())

· Exploration With Dplyr

· Labs for all sections

 

Day Three: Advanced Programming With R

· Statistical Modeling With R

§ Statistical Functions

§ Dealing With NA

§ Distributions (Binomial, Poisson, Normal)

· Regression

§ Introducing Linear Regressions

· Recommendations

· Text Processing (tm package / Wordclouds)

· Clustering

§ Introduction to Clustering

§ KMeans

· Classification

§ Introduction to Classification

§ Naive Bayes

§ Decision Trees

§ Training using caret package

§ Evaluating Algorithms

· R and Big Data

§ Hadoop

§ Big Data Ecosystem

§ RHadoop

· Labs for all sections

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