R Language

R Language

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    Introduction

    R is a programming language for numerical computing and graphics. It provides a variety of extensible graphical and statistical techniques. It is an innovation of the data scientist for analyzing the huge statistical data. It’s an important tool for advancement in machine learning spaces and numeric analysis. It is equipped with a unique feature that helps in processing, modeling and visualizing data. It is easy to learn and it takes only a few lines to write a complete code.

    COURSE SUMMARY

    Course Name R Language Online Training
    Contents Fundamentals and Programming techniques of R language
    Duration 30 Hours with Flexible timings
    Delivery Instructor Led-Live Online Training
    Eligibility Any Graduate
    Ideal For Aspiring job seekers in the field of Programming
    Next Batch Please visit the schedule section

    Course Objectives

    • Examine and Learn data manipulation with functions.
    • Learn the fundamentals of ‘R’ programming.
    • Apply Data Visualization to make fancy plots.
    • Apply Predictictive Analytics to predict outcomes.
    • Perform exploratory Data Analysis.
    • Implement various Data Importing techniques in R.
    • Examine and understand Sentiment Analysis.
    • Learn Machine Learning Techniques.
    • Learn the concept of Regression.
    • Implement Linear and Logistic Regression and understand Anova.

    PRE-REQUISITES:

    • Good Analytical Skills and Mathematical Skills

    Course Curriculum

    MODULE 1: OVERVIEW

    TOPICS: History of R, Downloading and Installing, Advantages and Disadvantages, How to Find Documentation

    MODULE 2: INTRODUCTION

    TOPICS: Using the R Console, Learning About the Environment, Getting Help, Saving Your Work, Writing and Executing Scripts

    MODULE 3: INSTALLING PACKAGES, DATA STRUCTURES AND VARIABLES

    TOPICS: Installing Resources, Finding Resources, Variables And Assignment, Data Types, Viewing Data and Summaries, Indexing, Naming Conventions, Sub Setting

    MODULE 4: GETTING DATA INTO THE R ENVIRONMENT

    TOPICS: Built-In Data, Reading Data Using ODBC, Reading Data from Structured Text Files

    MODULE 5: CONTROL FLOW

    TOPICS: Truth Testing, Looping, Branching, Vectorized Calculations

    MODULE 6: FUNCTIONS IN DEPTH

    TOPICS: Parameters, Variable Scope, Return Values, Exception Handling

    MODULE 7: HANDLING DATES IN R AND DESCRIPTIVE STATISTICS

    TOPICS: Formatting Dates for Modeling, Date and Date-Time Classes in R, Categorical Data, Continuous Data

    MODULE 8: INFERENTIAL STATISTICS

    TOPICS: Bivariate Correlation, Chi-Squared Test, T-Test and Non-Parametric Equivalents, Power Testing, Distribution Testing

    MODULE 9: GRAPHICS

    TOPICS: Base Graphics System In R, Exporting Graphics To Different Formats, Scatterplots, Dot Plots, Axes, Box And Whiskers, Bar Charts, Titles, Histograms, Legends, Understanding The Grammar Of Graphics, Labels, Building Graphics By Pieces, Quick Plot Function

    MODULE 10: LINEAR REGRESSION

    TOPICS: Linear Models, Interaction in Regression, Regression Plots, Scoring New Data from Models

    MODULE 11: ADVANCED MISSING DATA TECHNIQUES

    TOPICS: Study of Different Types of Missing Data, Multiple Imputation, AMELIA Package, Implications for Analysis

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