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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 data analytics and graphing with real world data.

Please note that this is an introductory-intermediate level class. And it is NOT a machine learning class

Objective

Learn to use R for data analytics

What You Will Learn

  • R language fundamentals
  • R data structures (Lists, Dataframes, Matrices)
  • Graphing with R
  • Analyzing datasets with R

Audience

Developers / data analytics

Duration

3 days

Format

Lectures and Hands-on

Pre-Requisites

  • Basic programming background is preferred

Setup

 

Detailed Outline

  • 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
      • Matrices
    • String and Text Manipulation
      • Character data type
      • File IO
    • Lists
    • Functions
      • Introducing Functions
      • Closures
      • lapply/sapply functions
    • DataFrames
    • Labs for all sections
  • 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
  • Analytics With R
    • Statistical Modeling With R
      • Statistical Functions
      • Dealing With NA
      • Distributions (Binomial, Poisson, Normal)
    • Data Exploration
    •  Regressions
      • Linear Regressions
      • Logistic Regressions
    • Text Processing (tm package / Wordclouds)
    • R and Big Data
    • Labs for all sections