Data Science with R Language - Curriculum

R Curriculum

About this Course

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing, which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

What are the pre-requisites?

There are no pre-requisites. No prior knowledge of Statistics, the language of R or analytic techniques is required.

Duration

22 to 25 Hours

Introduction to Data Science 

•       Introduction to Data, Tables, Database, ETL, EDW
•       What is Data Science?
•       Popular Tools
•       Role of Data Scientist
•       Analytics Methodology

R Introduction

•       Introduction to R Foundation
•       Software Installation on Various Operating Systems
•       Introduction to Real Time Applications
•       Introduction to Popular Packages

R Syntax

• ·      A gentle introduction to R expressions
• ·      Variables
• ·      Functions
• 
  
• 
  
• Vectors

• ·      Grouping values into vectors
• ·      Arithmetic Operations on Vectors
• ·      Graphs with them

Matrices

·      Creating and graphing two-dimensional data sets

Summary Statistics

·      Sum, Mean, Average, Minimum and Maximum

·      Median, Percentile and standard deviation

Factors

·      Creating and plotting categorized data

Data Frames

·      Organizing values into data frames

·      Loading frames from files and merging them

Working With Real-World Data

·      Testing for correlation between data sets

·      Linear models and installing additional packages

Probability Distribution

·      The Normal Distribution

·      The t Distribution

·      The Binomial Distribution

·      The Chi-Squared Distribution

Basic Plots

·      Strip Charts

·      Histograms

·      Boxplots

·      Scatter Plots

·      Normal QQ Plots

Intermediate Plotting

Continuous Data

·      Multiple Data Sets on One Plot

·      Error Bars

·      Adding Noise (jitter)

·      Multiple Graphs on One Image

·      Density Plots

·      Pairwise Relationships

·      Shaded Regions

·      Plotting a Surface

Discrete Data

·      Barplot

·      Mosaic Plot

Miscellaneous Options

·      Multiple Representations on One Plot

·      Multiple Windows

·      Print to A File

·      Annotation and Formatting

Indexing Into Vectors

·      Indexing with Logical

·      Not available or Missing Values

·      Indices with Logical Expressions

Two Way Tables

·      Creating a Table From Data

·      Creating a Table From Directory

·      Tools for Working with Tables

·      Graphical Views of Table

Data Management

·      Appending Data

·      Applying Functions Across Data Elements

Time Data Types

·      Time And Date Variables

·      Time Operations

Predictive Modeling Techniques

·      Linear Regression

·       Logistic Regression

·       Cluster Analysis

·       Decision Trees

·       Time Series Analysis