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 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|
|Ideal For||Aspiring job seekers in the field of Programming|
|Next Batch||Please visit the schedule section|
- 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.
- Good Analytical Skills and Mathematical Skills
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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