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Data Analytics with R

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Data Analytics With R

Overview

R is a 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.

What You Will Learn

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

Audience

Developers / data analytics

Duration

3 days

Format

Lectures and Hands-on

Pre-Requisites

  • Basic programming background is preferred

Setup

 

Detailed 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 3: 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


Upcoming Trainings

  • Please select a session and register.
  • No payment necessary for registration.
  • Payment is due 5 days before the class to secure the spot.