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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.

What You Will Learn

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

Note : This is not a machine learning class.
If interested in Machine Learning, please see   ‘Machine Learning Essentials‘ class.

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
    • Introducing R Language
    • Variables and Types
    • Control Structures (Loops / Conditionals)
    • Vectors, Matrices and Arrays
    • String and text manipulation using stringr
    • Lists
    • Levels  / Factors
    • Functions
    • apply functions
    • Labs for all sections
  • Day Two:
  • Intermediate R Programming
    • Dataframes
    • DataFrames and File I/O
    • Reading data from files
    • Data cleanup and preparation
    • Exploring built-in Datasets
    • Tidyverse packages – readr, tidyr, dplyr
    • Visualization
      • R-Graphics Package
      • ggplot2 package
    • Labs for all sections
    • Practice Lab : data analytics with tidyvserse
  • Day 3:
  • Advanced Analytics With R
    • Statistical Modeling
    • Covariance / Correlation / Covariance Matrix
    • Erros / Residuals
    • Feature Engineering
    • Distributions (Binomial, Poisson, Normal)
    • Text analytics – ngrams, context analytics
    • Regressions
      • Linear Regression
      • Logistic Regression
    • R and Big Data – Hadoop &  Spark
    • Labs for all sections
    • Practice Labs