Data Science Statistics and Analytics

Training in Chennai

Module 1

  • Overview
  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation

Module 2

  • Introduction
  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Saving your work

Module 3

  • Data Structures and Variables
  • Variables and assignment
  • Data types-Indexing, subsetting
  • Viewing data and summaries
  • Functions
  • Naming conventions
  • Objects
  • Models
  • Graphics

Module 4

  • Control Flow
  • Truth testing
  • Branching
  • Looping
  • Vectorized calculations

Module 5

  • Functions
  • Parameters
  • Return values
  • Variable scope
  • Exception Handling

Module 6

  • Getting Data into the R environment
  • Builtin data
  • Reading local data
  • Web data

Module 7

  • Overview of Statistics in R
  • Introduction to R Graphics
  • Model notation

Module 8

  • Descriptive statistics
  • Continuous data
  • Scatter plot,Box plot
  • Categorical data
  • Mosaic plot
  • Correlation

Module 9

  • Inferential statistics
  • T-test and non-parametric equivalents
  • Chi-squared test, logistic regression
  • Distribution testing
  • Power testing

Module 10

  • Linear Regression
  • Linear models
  • Regression plots
  • ANOVA

Module 11

  • Other Topics
  • Classification
  • Clustering
  • Time series
  • Dimensionality reduction
  • Machine Learning

Module 12

  • Object Oriented R
  • Generic functions
  • S3/S4 classes

Module 13

  • Installing Packages
  • Finding resources
  • Installing resources

Module 14

  • More about Graphics
  • Labels
  • Exporting graphics

Module 15

  • Sophisticated Graphics in R
  • Lattice
  • GGplot2
  • Interactive graphics
  • Animated GIF
  • rGGobi

Module 16

  • R for Mapping and GIS
  • Choropleth maps
  • Layers
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